Download Artificial Intelligence With Python [PDF],Newest Books
Download Download Artificial Intelligence With Python [PDF] Type: PDF Size: MB Download as PDF Download Original PDF This document was uploaded by user and they 27/01/ · Download Artificial Intelligence with Python Book in PDF, Epub and Kindle New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, Download Artificial Intelligence With Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Artificial Intelligence With Python book now. Artificial Intelligence With Python DOWNLOAD READ ONLINE Author: Prateek Joshi language: en Publisher: Release Date: Artificial Intelligence With Python (PYTHON) Prateek Joshi Artificial Intelligence with Python (PDF) (PYTHON) Prateek Joshi Artificial Intelligence with Python | Lord Laws - blogger.com blogger.com no longer ... read more
It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on.
We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization.
In every chapter, we explain an algorithm, implement it, and then build a smart application. New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3. x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key FeaturesCompletely updated and revised to Python 3. xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications.
This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications.
Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory. Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook Description Artificial intelligence AI plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research.
Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production. What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is for This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes.
Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask — and answer — tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning — whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate.
Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data — its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.
Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner.
The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem.
Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. An agent is said to act rationally if, given a set of rules, it takes actions to achieve its goals.
It just perceives and acts according to the information that's available. This system is used a lot in AI to design robots when they are sent to navigate unknown terrains. How do we define the right thing? The answer is that it depends on the objectives of the agent. The agent is supposed to be intelligent and independent. We want to impart the ability to adapt to new situations. It should understand its environment and then act accordingly to achieve an outcome that is in its best interests. The best interests are dictated by the overall goal it wants to achieve.
Let's see how an input gets converted to action: [ 20 ] Introduction to Artificial Intelligence How do we define the performance measure for a rational agent? One might say that it is directly proportional to the degree of success. The agent is set up to achieve a particular task, so the performance measure depends on what percentage of that task is complete. But we must think as to what constitutes rationality in its entirety. If it's just about results, can the agent take any action to get there? Making the right inferences is definitely a part of being rational, because the agent has to act rationally to achieve its goals.
This will help it draw conclusions that can be used successively. What about situations where there are no provably right things to do? There are situations where the agent doesn't know what to do, but it still has to do something. In this situation, we cannot include the concept of inference to define rational behavior. General Problem Solver The General Problem Solver GPS was an AI program proposed by Herbert Simon, J. Shaw, and Allen Newell. It was the first useful computer program that came into existence in the AI world. The goal was to make it work as a universal problem-solving machine. Of course there were many software programs that existed before, but these programs performed specific tasks. GPS was the first program that was intended to solve any general problem. GPS was supposed to solve all the problems using the same base algorithm for every problem.
As you must have realized, this is quite an uphill battle! To program the GPS, the authors created a new language called Information Processing Language IPL. The basic premise is to express any problem with a set of well-formed formulas. These formulas would be a part of a directed graph with multiple sources and sinks. In a graph, the source refers to the starting node and the sink refers to the ending node. In the case of GPS, the source refers to axioms and the sink refers to the conclusions. Even though GPS was intended to be a general purpose, it could only solve well-defined problems, such as proving mathematical theorems in geometry and logic. It could also solve word puzzles and play chess. The reason was that these problems could be formalized to a reasonable extent. But in the real world, this quickly becomes intractable because of the number of possible paths you can take. If it tries to brute force a problem by counting the number of walks in a graph, it becomes computationally infeasible.
The first step is to define the goals. Let's say our goal is to get some milk from the grocery store. The next step is to define the preconditions. These preconditions are in reference to the goals. To get milk from the grocery store, we need to have a mode of transportation and the grocery store should have milk available. After this, we need to define the operators. If my mode of transportation is a car and if the car is low on fuel, then we need to ensure that we can pay the fueling station. We need to ensure that you can pay for the milk at the store.
An operator takes care of the conditions and everything that affects them. It consists of actions, preconditions, and the changes resulting from taking actions. In this case, the action is giving money to the grocery store. Of course, this is contingent upon you having the money in the first place, which is the precondition. By giving them the money, you are changing your money condition, which will result in you getting the milk. GPS will work as long as you can frame the problem like we did just now. The constraint is that it uses the search process to perform its job, which is way too computationally complex and time consuming for any meaningful real-world application. Building an intelligent agent There are many ways to impart intelligence to an agent. The most commonly used techniques include machine learning, stored knowledge, rules, and so on. In this section, we will focus on machine learning.
In this method, the way we impart intelligence to an agent is through data and training. By going through the data and the associated labels, the machine learns how to extract patterns and relationships. In the preceding example, the intelligent agent depends on the learning model to run the inference engine. Once the sensor perceives the input, it sends it to the feature extraction block. Once the relevant features are extracted, the trained inference engine performs a prediction based on the learning model. This learning model is built using machine learning. The inference engine then takes a decision and sends it to the actuator, which then takes the required action in the real world. There are many applications of machine learning that exist today. It is used in image recognition, robotics, speech recognition, predicting stock market behavior, and so on. In order to understand machine learning and build a complete solution, you will have to be familiar with many techniques from different fields such as pattern recognition, artificial neural networks, data mining, statistics, and so on.
Before we had machines that could compute, people used to rely on analytical models. These models were derived using a mathematical formulation, which is basically a sequence of steps followed to arrive at a final equation. The problem with this approach is that it was based on human judgment. Hence these models were simplistic and inaccurate with just a few parameters. We then entered the world of computers. These computers were good at analyzing data. So, people increasingly started using learned models. These models are obtained through the process of training. During training, the machines look at many examples of inputs and outputs to arrive at the equation. These learned models are usually complex and accurate, with thousands of parameters. This gives rise to a very complex mathematical equation that governs the data. Machine Learning allows us to obtain these learned models that can be used in an inference engine. One of the best things about this is the fact that we don't need to derive the underlying mathematical formula.
You don't need to know complex mathematics, because the machine derives the formula based on data. All we need to do is create the list of inputs and the corresponding outputs. The learned model that we get is just the relationship between labeled inputs and the desired outputs. Installing Python 3 We will be using Python 3 throughout this book. Make sure you have installed the latest version of Python 3 on your machine. x where x. x are version numbers printed on your terminal, you are good to go. If not, installing it is pretty straightforward. xx and above. Installing on Mac OS X If you are on Mac OS X, it is recommended that you use Homebrew to install Python 3. It is a great package installer for Mac OS X and it is really easy to use. Anaconda is pretty popular and easy to use.
The good part about these distributions is that they come with all the necessary packages preinstalled. If you use one of these versions, you don't need to install the packages separately. Installing packages During the course of this book, we will use various packages such as NumPy, SciPy, scikitlearn, and matplotlib. Make sure you install these packages before you proceed. If you use Ubuntu or Mac OS X, installing these packages is pretty straightforward. All these packages can be installed using a one-line command on the terminal. html If you are on Windows, you should have installed a SciPy-stack compatible version of Python 3. Now that we have installed the necessary Python packages, let's see how to use the packages to interact with data. data You will see an output like this printed on your Terminal: [ 27 ] Introduction to Artificial Intelligence Let's check out the labels: You will see the following printed on your Terminal: The actual array is larger, so the image represents the first few values in that array.
There are also image datasets available in the scikit-learn package. Each image is of shape 8×8. images[4] [ 28 ] Introduction to Artificial Intelligence You will see the following on your Terminal: As you can see, it has eight rows and eight columns. Summary In this chapter, we learned what AI is all about and why we need to study it. We discussed various applications and branches of AI. We understood what the Turing test is and how it's conducted. We learned how to make machines think like humans. We discussed the concept of rational agents and how they should be designed. We learned about General Problem Solver GPS and how to solve a problem using GPS. We discussed how to develop an intelligent agent using machine learning.
We covered different types of models as well. We discussed how to install Python 3 on various operating systems. We learned how to install the necessary packages required to build AI applications. We discussed how to use the packages to load data that's available in scikit-learn. In the next chapter, we will learn about supervised learning and how to build models for classification and regression. By the end of this chapter, you will know about these topics: What is the difference between supervised and unsupervised learning? What is classification? How to preprocess data using various methods What is label encoding? How to build a logistic regression classifier What is Naïve Bayes classifier? What is a confusion matrix?
What are Support Vector Machines and how to build a classifier based on that? What is linear and polynomial regression? How to build a linear regressor for single variable and multivariable data How to estimate housing prices using Support Vector Regressor Supervised versus unsupervised learning One of the most common ways to impart artificial intelligence into a machine is through machine learning. The world of machine learning is broadly divided into supervised and unsupervised learning. There are other divisions too, but we'll discuss those later.
Classification and Regression Using Supervised Learning Supervised learning refers to the process of building a machine learning model that is based on labeled training data. For example, let's say that we want to build a system to automatically predict the income of a person, based on various parameters such as age, education, location, and so on. To do this, we need to create a database of people with all the necessary details and label it. By doing this, we are telling our algorithm what parameters correspond to what income. Based on this mapping, the algorithm will learn how to calculate the income of a person using the parameters provided to it.
Unsupervised learning refers to the process of building a machine learning model without relying on labeled training data. In some sense, it is the opposite of what we just discussed in the previous paragraph. Since there are no labels available, you need to extract insights based on just the data given to you. For example, let's say that we want to build a system where we have to separate a set of data points into multiple groups. The tricky thing here is that we don't know exactly what the criteria of separation should be. Hence, an unsupervised learning algorithm needs to separate the given dataset into a number of groups in the best way possible. In this chapter, we will discuss supervised classification techniques. The process of classification is one such technique where we classify data into a given number of classes. During classification, we arrange data into a fixed number of categories so that it can be used most effectively and efficiently.
In machine learning, classification solves the problem of identifying the category to which a new data point belongs. We build the classification model based on the training dataset containing data points and the corresponding labels. For example, let's say that we want to check whether the given image contains a person's face or not. We would build a training dataset containing classes corresponding to these two classes: face and no-face. We then train the model based on the training samples we have. This trained model is then used for inference. A good classification system makes it easy to find and retrieve data. This is used extensively in face recognition, spam identification, recommendation engines, and so on. The algorithms for data classification will come up with the right criteria to separate the given data into the given number of classes. If there is an insufficient number of samples, then the algorithm will overfit to the training data.
This means that it won't perform well on unknown data because it fine-tuned the model too much to fit into the patterns observed in training data. This is actually a very common problem that occurs in the world of machine learning. It's good to consider this factor when you build various machine learning models. Preprocessing data We deal with a lot of raw data in the real world. Machine learning algorithms expect data to be formatted in a certain way before they start the training process. In order to prepare the data for ingestion by machine learning algorithms, we have to preprocess it and convert it into the right format. Let's see how to do it. array [[5. Let's start with binarization: Binarization Mean removal Scaling Normalization Let's take a look at each technique, starting with the first. Binarization This process is used when we want to convert our numerical values into boolean values. Let's use an inbuilt method to binarize input data using 2. The remaining values become 0.
Mean removal Removing the mean is a common preprocessing technique used in machine learning. It's usually useful to remove the mean from our feature vector, so that each feature is centered on zero. We do this in order to remove bias from the features in our feature vector. Scaling In our feature vector, the value of each feature can vary between many random values. So it becomes important to scale those features so that it is a level playing field for the machine learning algorithm to train on. We don't want any feature to be artificially large or small just because of the nature of the measurements.
data: 0. In machine learning, we use many different forms of normalization. Some of the most common forms of normalization aim to modify the values so that they sum up to 1. L1 normalization, which refers to Least Absolute Deviations, works by making sure that the sum of absolute values is 1 in each row. L2 normalization, which refers to least squares, works by making sure that the sum of squares is 1. In general, L1 normalization technique is considered more robust than L2 normalization technique. L1 normalization technique is robust because it is resistant to outliers in the data. A lot of times, data tends to contain outliers and we cannot do anything about it. We want to use techniques that can safely and effectively ignore them during the calculations.
If we are solving a problem where outliers are important, then maybe L2 normalization becomes a better choice. py file. These labels can be in the form of words, numbers, or something else. The machine learning functions in sklearn expect them to be numbers. So if they are already numbers, then we can use them directly to start training. But this is not usually the case. In the real world, labels are in the form of words, because words are human readable. We label our training data with words so that the mapping can be tracked. To convert word labels into numbers, we need to use a label encoder. Label encoding refers to the process of transforming the word labels into numerical form.
This enables the algorithms to operate on our data. LabelEncoder encoder. Logistic Regression classifier Logistic regression is a technique that is used to explain the relationship between input variables and output variables. The input variables are assumed to be independent and the output variable is referred to as the dependent variable. The dependent variable can take only a fixed set of values. These values correspond to the classes of the classification problem. Our goal is to identify the relationship between the independent variables and the dependent variables by estimating the probabilities using a logistic function. This logistic function is a sigmoid curve that's used to build the function with various parameters. It is very closely related to generalized linear model analysis, where we try to fit a line to a bunch of points to minimize the error.
Instead of using linear regression, we use logistic regression. Logistic regression by itself is actually not a classification technique, but we use it in this way so as to facilitate classification. It is used very commonly in machine learning because of its simplicity. Let's see how to build a classifier using logistic regression. Make sure you have Tkinter package installed on your system before you proceed. We will be importing a function from the file utilities. We will be looking into that function very soon. array [[3. array [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3] We will train the classifier using this labeled data. We will be using this multiple times in this chapter, so it's better to define it in a separate file and import the function. This function is given in the utilities. py file provided to you. Create a new Python file and import the following packages: import numpy as np import matplotlib.
min - 1. This grid is basically a set of values that is used to evaluate the function, so that we can visualize the boundaries of the classes. meshgrid np. predict np. shape Create the figure, pick a color scheme, and overlay all the points: Create a plot plt. figure Choose a color scheme for the plot plt. gray Overlay the training points on the plot plt. Paired [ 39 ] Classification and Regression Using Supervised Learning Specify the boundaries of the plots using the minimum and maximum values, add the tick marks, and display the figure: Specify the boundaries of the plot plt. max plt. max Specify the ticks on the X and Y axes plt. xticks np. arange int X[:, 0]. min - 1 , int X[:, 0].
yticks np. arange int X[:, 1]. min - 1 , int X[:, 1]. You should be careful with this parameter, because if you increase it by a lot, it will overfit to the training data and it won't generalize well. If you run the code with C set to , you will see the following screenshot: If you compare with the earlier figure, you will see that the boundaries are now better. Bayes theorem describes the probability of an event occurring based on different conditions that are related to this event. We build a Naïve Bayes classifier by assigning class labels to problem instances. These problem instances are represented as vectors of feature values. The assumption here is that the value of any given feature is independent of the value of any other feature. This is called the independence assumption, which is the naïve part of a Naïve Bayes classifier. Given the class variable, we can just see how a given feature affects, it regardless of its affect on other features. For example, an animal may be considered a cheetah if it is spotted, has four legs, has a tail, and runs at about 70 MPH.
A Naïve Bayes classifier considers that each of these features contributes independently to the outcome. The outcome refers to the probability that this animal is a cheetah. We don't concern ourselves with the correlations that may exist between skin patterns, number of legs, presence of a tail, and movement speed. Let's see how to build a Naïve Bayes classifier. pyplot as plt from sklearn. txt as the source of data. We will be using the Gaussian Naïve Bayes classifier here. We need to perform cross validation, so that we don't use the same training data when we are testing it. Split the data into training and testing subsets. We can see that they separate the 4 clusters well and create regions with boundaries based on the distribution of the input datapoints.
Confusion matrix A Confusion matrix is a figure or a table that is used to describe the performance of a classifier. It is usually extracted from a test dataset for which the ground truth is known. We compare each class with every other class and see how many samples are misclassified. During the construction of this table, we actually come across several key metrics that are very important in the field of machine learning. Let's consider a binary classification case where the output is either 0 or 1: True positives: These are the samples for which we predicted 1 as the output and the ground truth is 1 too.
True negatives: These are the samples for which we predicted 0 as the output and the ground truth is 0 too. False positives: These are the samples for which we predicted 1 as the output but the ground truth is 0. This is also known as a Type I error. False negatives: These are the samples for which we predicted 0 as the output but the ground truth is 1. This is also known as a Type II error. Depending on the problem at hand, we may have to optimize our algorithm to reduce the false positive or the false negative rate. For example, in a biometric identification system, it is very important to avoid false positives, because the wrong people might get access to sensitive information. Let's see how to create a confusion matrix.
gray plt. title 'Confusion matrix' plt. arange 5 plt. xticks ticks, ticks plt. yticks ticks, ticks plt. ylabel 'True labels' plt. xlabel 'Predicted labels' plt. show In the above visualization code, the ticks variable refers to the number of distinct classes. In our case, we have five distinct labels. If you run the code, you will see the following screenshot: [ 48 ] Classification and Regression Using Supervised Learning White indicates higher values, whereas black indicates lower values as seen on the color map slider. In an ideal scenario, the diagonal squares will be all white and everything else will be black. Support Vector Machines A Support Vector Machine SVM is a classifier that is defined using a separating hyperplane between the classes. This hyperplane is the N-dimensional version of a line. Given labeled training data and a binary classification problem, the SVM finds the optimal hyperplane that separates the training data into two classes.
This can easily be extended to the problem with N classes. Let's consider a two-dimensional case with two classes of points. Given that it's 2D, we only have to deal with points and lines in a 2D plane. This is easier to visualize than vectors and hyperplanes in a high-dimensional space. Of course, this is a simplified version of the SVM problem, but it is important to understand it and visualize it before we can apply it to highdimensional data. But how do we define optimal? In this picture, the solid line represents the best hyperplane. You can draw many different lines to separate the two classes of points, but this line is the best separator, because it maximizes the distance of each point from the separating line.
The points on the dotted lines are called Support Vectors. The perpendicular distance between the two dotted lines is called maximum margin. Hence this is a binary classification problem. One thing to note in this dataset is that each datapoint is a mixture of words and numbers. We cannot use the data in its raw format, because the algorithms don't know how to deal with words. We cannot convert everything using label encoder because numerical data is valuable. Hence we need to use a combination of label encoders and raw numerical data to build an effective classifier. pyplot as plt from sklearn import preprocessing from sklearn. svm import LinearSVC from sklearn. txt to load the data. txt' In order to load the data from the file, we need to preprocess it so that we can prepare it for classification. The last element in each line represents the label. array X If any attribute is a string, then we need to encode it.
If it is a number, we can keep it as it is. empty X. shape for i,item in enumerate X[0] : if item. append preprocessing. Once it's done, you will see the following printed on your Terminal: F1 score: This construction is used in various forms throughout logic programming to solve various types of problems. Let's go ahead and see how to solve these problems in Python. Installing Python packages Before we start logic programming in Python, we need to install a couple of packages. The package logpy is a Python package that enables logic programming in Python. We will also be using SymPy for some of the problems. Once you have successfully installed these packages, you can proceed to the next section.
Matching mathematical expressions We encounter mathematical operations all the time. Logic programming is a very efficient way of comparing expressions and finding out unknown values. Let's see how to do that. Create a new Python file and import the following packages: from logpy import run, var, fact import logpy. Let's specify that: Declare that these operations are commutative using the facts system fact la. commutative, mul fact la. commutative, add fact la. associative, mul fact la. The method run is commonly used in logpy.
This method takes the input arguments and runs the expression. The first argument is the number of values, the second argument is a variable, and the third argument is a function: Compare expressions print run 0, a, b, c , la. If you run the code, you will see the following output on your Terminal: 3, -1, -2 , 3, -1, -2 , The three values in the first two lines represent the values for a, b, and c. The first two expressions matched with the original expression, whereas the third one returned nothing. This is because even though the third expression is mathematically the same, it is structurally different. Pattern comparison works by comparing the structure of the expressions. Validating primes Let's see how to use logic programming to check for prime numbers. We will use the constructs available in logpy to determine which numbers in the given list are prime, as well as finding out if a given number is a prime or not. Create a new Python file and import the following packages: import itertools as it import logpy.
core as lc from sympy. generate import prime, isprime [ ] Logic Programming Next, define a function that checks if the given number is prime depending on the type of data. If it's a number, then it's pretty straightforward. If it's a variable, then we have to run the sequential operation. To give a bit of background, the method conde is a goal constructor that provides logical AND and OR operations. isvar x : return lc. condeseq [ lc. eq, x, p ] for p in map prime, it. count 1 else: return lc. success if isprime x else lc. var Define a set of numbers and check which numbers are prime. run 0, x, lc.
If you run the code, you will see the following output: List of primes in the list: {3, 11, 13, 17, 19, 23, 29} List of first 7 prime numbers: 2, 3, 5, 7, 11, 13, 17 You can confirm that the output values are correct. Consider the following family tree: John and Megan have three sons — William, David, and Adam. The wives of William, David, and Adam are Emma, Olivia, and Lily respectively. William and Emma have two children — Chris and Stephanie. David and Olivia have five children — Wayne, Tiffany, Julie, Neil, and Peter. Adam and Lily have one child — Sophia. Based on these facts, we can create a program that can tell us the name of Wayne's grandfather or Sophia's uncles are. Even though we have not explicitly specified anything about the grandparent or uncle relationships, logic programming can infer them. These relationships are specified in a file called relationships. json provided for you. The file looks like the following: { "father": [ {"John": "William"}, {"John": "David"}, {"John": "Adam"}, {"William": "Chris"}, {"William": "Stephanie"}, {"David": "Wayne"}, {"David": "Tiffany"}, {"David": "Julie"}, {"David": "Neil"}, {"David": "Peter"}, {"Adam": "Sophia"} [ ] Logic Programming ], "mother": [ {"Megan": "William"}, {"Megan": "David"}, {"Megan": "Adam"}, {"Emma": "Stephanie"}, {"Emma": "Chris"}, {"Olivia": "Tiffany"}, {"Olivia": "Julie"}, {"Olivia": "Neil"}, {"Olivia": "Peter"}, {"Lily": "Sophia"} ] } It is a simple json file that specifies only the father and mother relationships.
Note that we haven't specified anything about husband and wife, grandparents, or uncles. Create a new Python file and import the following packages: import json from logpy import Relation, facts, run, conde, var, eq Define a function to check if x is the parent of y. We will use the logic that if x is the parent of y, then x is either the father or the mother. We will use the logic that if x is the sibling of y, then x and y will have the same parents. Notice that there is a slight modification needed here because when we list out all the siblings of x, x will be listed as well because x satisfies these conditions. So when we print the output, we will have to remove x from the list. We will use the logic that if x is y's uncle, then x grandparents will be the same as y's parents. Notice that there is a slight modification needed here because when we list out all the uncles of x, x's father will be listed as well because x's father satisfies these conditions.
So when we print the output, we will have to remove x's father from the list. json file: with open 'relationships. loads f. read Read the data and add them to our fact base: for item in d['father']: facts father, list item. keys [0], list item. values [0] for item in d['mother']: facts mother, list item. If you run the code, you will see many things on your Terminal. The first half looks like the following: The second half looks like the following: [ ] Logic Programming You can compare the outputs with the family tree to ensure that the answers are indeed correct. Analyzing geography Let's use logic programming to build a solver to analyze geography.
In this problem, we will specify information about the location of various states in the US and then query our program to answer various questions based on those facts and rules. These files contain the details about which states are adjacent to each other and which states are coastal. Based on this, we can get interesting information like What states are adjacent to both Oklahoma and Texas? or Which coastal state is adjacent to both New Mexico and Louisiana? split ',' for line in f if line and line[0]. Check if Nevada is adjacent to Louisiana: Is Nevada adjacent to Louisiana? If you run the code, you will see the following output: You can cross-check the output with the US map to verify if the answers are right.
You can also add more questions to the program to see if it can answer them. Building a puzzle solver Another interesting application of logic programming is in solving puzzles. We can specify the conditions of a puzzle and the program will come up with a solution. In this section, we will specify various bits and pieces of information about four people and ask for the missing piece of information. In the logic program, we specify the puzzle as follows: Steve has a blue car The person who owns the cat lives in Canada Matthew lives in USA The person with the black car lives in Australia Jack has a cat Alfred lives in Australia The person who has a dog lives in France Who has a rabbit? Who is that person? Who has a rabbit? join [str x for x in item] The full code is given in puzzle.
If you run the code, you will see the following output: The preceding figure shows all the values obtained using the solver. Some of them are still unknown as indicated by numbered names. Even though the information was incomplete, our solver was able to answer our question. But in order to answer every single question, you may need to add more rules. This program was to demonstrate how to solve a puzzle with incomplete information. You can play around with it and see how you can build puzzle solvers for various scenarios. We discussed how various programming paradigms deal with building programs. We understood how programs are built in logic programming. We learned about various building blocks of logic programming and discussed how to solve problems in this domain. We implemented various Python programs to solve interesting problems and puzzles.
In the next chapter, we will learn about heuristic search techniques and use those algorithms to solve real world problems. Heuristic search techniques are used to search through the solution space to come up with answers. The search is conducted using heuristics that guide the search algorithm. This heuristic allows the algorithm to speed up the process, which would otherwise take a really long time to arrive at the solution. By the end of this chapter, you will know about the following: What is heuristic search? Uninformed vs. informed search Constraint Satisfaction Problems Local search techniques Simulated annealing Constructing a string using greedy search Solving a problem with constraints Solving the region coloring problem Building an 8-puzzle solver Building a maze solver What is heuristic search?
Searching and organizing data is an important topic within Artificial Intelligence. There are many problems that require searching for an answer within the solution domain. There are many possible solutions to a given problem and we do not know which ones are correct. By efficiently organizing the data, we can search for solutions quickly and effectively. Heuristic Search Techniques More often, there are so many possible options to solve a given problem that no algorithm can be developed to find a right solution. Also, going through every single solution is not possible because it is prohibitively expensive.
In such cases, we rely on a rule of thumb that helps us narrow down the search by eliminating the options that are obviously wrong. This rule of thumb is called a heuristic. The method of using heuristics to guide our search is called heuristic search. Heuristic techniques are very handy because they help us speed up the process. Even if the heuristic is not able to completely eliminate some options, it will help us to order those options so that we are more likely to get to the better solutions first. Uninformed versus Informed search If you are familiar with computer science, you should have heard about search techniques like Depth First Search DFS , Breadth First Search BFS , and Uniform Cost Search UCS. These are search techniques that are commonly used on graphs to get to the solution. These are examples of uninformed search. They do not use any prior information or rules to eliminate some paths. They check all the plausible paths and pick the optimal one.
Heuristic search, on the other hand, is called Informed search because it uses prior information or rules to eliminate unnecessary paths. Uninformed search techniques do not take the goal into account. These techniques don't really know where they are trying to go unless they just stumble upon the goal in the process. In the graph problem, we can use heuristics to guide the search. For example, at each node, we can define a heuristic function that returns a score that represents the estimate of the cost of the path from the current node to the goal. By defining this heuristic function, we are informing the search technique about the right direction to reach the goal. This will allow the algorithm to identify which neighbor will lead to the goal. We need to note that heuristic search might not always find the most optimal solution.
This is because we are not exploring every single possibility and we are relying on a heuristic. But it is guaranteed to find a good solution in a reasonable time, which is what we expect from a practical solution. In real-world scenarios, we need solutions that are fast and effective. Heuristic searches provide an efficient solution by arriving at a reasonable solution quickly. They are used in cases where the problems cannot be solved in any other way or would take a really long time to solve. These constraints are basically conditions that cannot be violated during the process of solving the problem. These problems are referred to as Constraint Satisfaction Problems CSPs. CSPs are basically mathematical problems that are defined as a set of variables that must satisfy a number of constraints.
When we arrive at the final solution, the states of the variables must obey all the constraints. This technique represents the entities involved in a given problem as a collection of a fixed number of constraints over variables. These variables need to be solved by constraint satisfaction methods. These problems require a combination of heuristics and other search techniques to be solved in a reasonable amount of time. In this case, we will use constraint satisfaction techniques to solve problems on finite domains. A finite domain consists of a finite number of elements. Since we are dealing with finite domains, we can use search techniques to arrive at the solution. Local search techniques Local search is a particular way of solving a CSP.
It keeps improving the values until all the constraints are satisfied. It iteratively keeps updating the variables until we arrive at the destination. These algorithms modify the value during each step of the process that gets us closer to the goal. In the solution space, the updated value is closer to the goal than the previous value. Hence it is known as a local search. Local search algorithm is a heuristic search algorithm. These algorithms use a function that calculates the quality of each update. For example, it can count the number of constraints that are being violated by the current update or it can see how the update affects the distance to the goal. This is referred to as the cost of the assignment. The overall goal of local search is to find the minimal cost update at each step. Hill climbing is a popular local search technique. It uses a heuristic function that measures the difference between the current state and the goal.
When we start, it checks if the state is the final goal. If it is, then it stops. If not, then it selects an update and generates a new state. If it's closer to the goal than the current state, then it makes that the current state. If not, it ignores it and continues the process until it checks all possible updates. It basically climbs the hill until it reaches the summit. Stochastic search techniques are used extensively in various fields such as robotics, chemistry, manufacturing, medicine, economics, and so on. We can perform things like optimizing the design of a robot, determining the timing strategies for automated control in factories, and planning traffic. Stochastic algorithms are used to solve many real-world problems.
Simulated Annealing is a variation of the hill climbing technique. One of the main problems of hill climbing is that it ends up climbing false foothills. This means that it gets stuck in local maxima. So it is better to check out the whole space before we make any climbing decisions. In order to achieve this, the whole space is initially explored to see what it is like. This helps us avoid getting stuck in a plateau or local maxima. In Simulated Annealing, we reformulate the problem and solve it for minimization, as opposed to maximization.
So, we are now descending into valleys as opposed to climbing hills. We are pretty much doing the same thing, but in a different way. We use an objective function to guide the search. This objective function serves as our heuristic. The reason it is called Simulated Annealing is because it is derived from the metallurgical process. We first heat metals up and then let them cool until they reach the optimal energy state. The rate at which we cool the system is called the annealing schedule. The rate of cooling is important because it directly impacts the final result. In the real world case of metals, if the rate of cooling is too fast, it ends up settling for the local maximum. For example, if we take the heated metal and put it in cold water, it ends up quickly settling for the sub-optimal local maximum. If the rate of cooling is slow and controlled, we give the metal a chance to arrive at the globally optimum state. The chances of taking big steps quickly towards any particular hill are lower in this case.
Since the rate of cooling is slow, it will take its time to choose the best state. We do something similar with data in our case. If it is, then we stop. If not, then we set the best state variable to the current state. We then define our annealing schedule that controls how quickly it descends into a valley. We compute the difference between the current state and the new state. If the new state is not better, then we make it the current state with a certain predefined probability. We do this using a random number generator and making a decision based on a threshold. If it is above the threshold, then we set the best state to this state. Based on this, we update the annealing schedule depending on the number of nodes. We keep doing this until we arrive at the goal. Constructing a string using greedy search Greedy search is an algorithmic paradigm that makes the locally optimal choice at each stage in order to find the global optimum.
But in many problems, greedy algorithms do not produce globally optimum solutions. An advantage of using greedy algorithms is that they produce an approximate solution in a reasonable time. The hope is that this approximate solution is reasonably close to the global optimal solution. Greedy algorithms do not refine their solutions based on new information during the search. For example, let's say you are planning on a road trip and you want to take the best route possible. If you use a greedy algorithm to plan the route, it would ask you to take routes that are shorter but might end up taking more time. It can also lead you to paths that may seem faster in the short term, but might lead to traffic jams later. This happens because greedy algorithms only think about the next step and not the globally optimal final solution.
Let's see how to solve a problem using a greedy search. In this problem, we will try to recreate the input string based on the alphabets. We will ask the algorithm to search the solution space and construct a path to the solution. We will be using a package called simpleai throughout this chapter. It contains various routines that are useful in building solutions using heuristic search techniques. We need to make a few changes to the source code in order to make it work in Python3. A file called simpleai. zip has been provided along with the code for the book. Unzip this file into a folder called simpleai.
Download Artificial Intelligence With Python full books in PDF, epub, and Kindle. Read online free Artificial Intelligence With Python ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available! Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code.
It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data.
It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios.
If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3. x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key FeaturesCompletely updated and revised to Python 3. xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.
Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.
Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook Description Artificial intelligence AI plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning.
As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production. What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is for This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes.
Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask — and answer — tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning — whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate.
Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data — its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.
Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.
Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment.
Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering.
Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students. Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today.
Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand. This practical guide provides nearly self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. The widespread adoption of AI and machine learning is revolutionizing many industries today.
Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence AGI and superintelligence SI Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about.
Skip to content. Artificial Intelligence With Python Download Artificial Intelligence With Python full books in PDF, epub, and Kindle. Artificial Intelligence with Python. Author : Prateek Joshi Publsiher : Packt Publishing Ltd Total Pages : Release : Genre : Computers ISBN : GET BOOK. Download Artificial Intelligence with Python Book in PDF, Epub and Kindle. Author : Alberto Artasanchez,Prateek Joshi Publsiher : Packt Publishing Ltd Total Pages : Release : Genre : Computers ISBN : GET BOOK. Artificial Intelligence with Python Cookbook.
Author : Ben Auffarth Publsiher : Packt Publishing Ltd Total Pages : Release : Genre : Computers ISBN : GET BOOK. Download Artificial Intelligence with Python Cookbook Book in PDF, Epub and Kindle. Python Machine Learning. Author : Sebastian Raschka Publsiher : Packt Publishing Ltd Total Pages : Release : Genre : Computers ISBN : GET BOOK. Download Python Machine Learning Book in PDF, Epub and Kindle. Practical Machine Learning with Python. Author : Dipanjan Sarkar,Raghav Bali,Tushar Sharma Publsiher : Apress Total Pages : Release : Genre : Computers ISBN : GET BOOK. Download Practical Machine Learning with Python Book in PDF, Epub and Kindle. Machine Learning with Python Cookbook. Author : Chris Albon Publsiher : "O'Reilly Media, Inc. Download Machine Learning with Python Cookbook Book in PDF, Epub and Kindle. Artificial Intelligence in Finance. Author : Yves Hilpisch Publsiher : "O'Reilly Media, Inc.
Download Artificial Intelligence in Finance Book in PDF, Epub and Kindle.
(PYTHON) Prateek Joshi Artificial Intelligence with Python,Download Free PDF / Read Online
09/01/ · Artificial Neural Networks Reinforcement Learning Deep Learning with Convolutional Neural Networks Download Free PDF / Read Online Author (s): Prateek Download and istall the latest Python 3 release from blogger.com This should also install pip3. You can install matplotlib using pip3 install matplotlib in a terminal shell (not in Python). Artificial Intelligence With Python DOWNLOAD READ ONLINE Author: Prateek Joshi language: en Publisher: Release Date: Artificial Intelligence With Python 11/09/ · Artificial Intelligence and Big Data Download Free PDF / Read Online Author (s): Alberto Artasanchez, Prateek Joshi Publisher: Packt Publishing Published: January Download Artificial Intelligence With Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Artificial Intelligence With Python book now. Artificial intelligence with the help of neural networks can analyze the data more deeply. Due to this capability, AI can think and respond to the situations which are based on the conditions in ... read more
We will see various applications, including expression matching, parsing family trees, and solving puzzles. Once it's done, you will see the following printed on your Terminal: F1 score: Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Learn from new data and update constantly using the right learning algorithms. Of course this is easier said than done! Building an 8-puzzle solver 8-puzzle is a variant of the puzzle.
These sensors can see things in front of them and measure the temperature, heat, movements, and so on. FitnessMin Create the toolbox and register the functions. This will ensure diversity among artificial intelligence with python pdf download decision trees. Since the output variable is continuous valued, we need to build a regressor that can predict the output. Sometimes, a few features are completely redundant.
No comments:
Post a Comment