Best Machine Learning books & Best Machine Learning courses 2024

Best Machine Learning Books 2023

Best Machine Learning Courses 2023

 

Best Machine Learning tutorials 2023

Machine Learning A-Z: Hands-On Python & R In Data Science

Machine Learning A-Z: Hands-On Python & R In Data Science by Kirill Eremenko, Hadelin de Ponteves and SuperDataScience Team will teach you Machine Learning using Python & R. This course has been designed by two professional Data Scientists. With over 300,000 students and an average rating of 4.5 on Udemy, this is quite simply one of the best Machine Learning & Python courses. If that wasn’t enough, this course has a length of over 40 hours of video content! This makes it one of the most comprehensive Machine Learning courses ever.

The Machine Learning Python course is structured in the following way:

• Part 1 – Data Preprocessing
• Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
• Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
• Part 4 – Clustering: K-Means, Hierarchical Clustering
• Part 5 – Association Rule Learning: Apriori, Eclat
• Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
• Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
• Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
• Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

This Python tutorial will teach you everything related to Machine Learning, step-by-step. You will build an army of powerful Machine Learning models. Then you will combine them to solve any problem. You will be able to handle different topics like Reinforcement Learning, NLP and Deep Learning. Advanced techniques like Dimensionality Reduction are also taught.Using the knowledge you gain, you will know which Machine Learning model to use depending on the problem. Learn Machine Learning from the best Machine Learning tutorial in 2023.

You will learn:
Master machine learning on Python and R
Have a great intuition of many machine learning models
Make accurate predictions
Do a powerful analysis
Build robust machine learning models
Create high added value for your business
Use machine learning for personal gain
Cover specific topics such as reinforcement learning, NLP, and deep learning
Manage advanced techniques such as dimensionality reduction
Know which machine learning model to choose for each type of problem
Build an army of powerful machine learning models and know how to combine them to solve any problem

Python for Data Science and Machine Learning Bootcamp

How to use Python for Data Science and Machine Learning. You will use different Python frameworks and libraries such as NumPy, Pandas, Seaborn, Matplotlib, Scikit-Learn, Tensorflow and more. This Python tutorial will show you how to use Python to implement Machine Learning algorithms.  You will use SciKit-Learn for Machine Learning. This tutorial will show you how use Matplotlib and Seaborn for data visualizations. Use Spark for Big Data analysis. You will understand what Natural Language Processing is along with Spam Filters. K Nearest Neighbors and K Means Clustering are discussed. You will learn all about Neural Networks. This Python data Science training will teach you how to support Vector Machines. Decision Trees and Random Forests are both explained. This is one of the best Data Science Python courses in 2023.

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked number one on Glassdoor and the average salary for a data scientist exceeds $ 120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems! This course is designed for beginners with some programming experience or for seasoned developers looking to take the leap into data science! This comprehensive course is comparable to other Data Science bootcamps which typically cost thousands of dollars, but now you can learn all of this information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for each lecture, this is one of the most comprehensive data science and machine learning courses on Udemy!

You will learn

Use Python for data science and machine learning
Use Spark for Big Data Analysis
Implement machine learning algorithms
Learn how to use NumPy for numeric data
Learn how to use Pandas for data analysis
Learn how to use Matplotlib for Python plotting
Learn how to use Seaborn for statistical graphs
Use Plotly for interactive dynamic visualizations
Using SciKit-Learn for Machine Learning Tasks
K-Means clustering
Logistic regression
Linear regression
Random forest and decision trees
Natural language processing and spam filters
Neural networks
Support vector machines

Data Science and Machine Learning Bootcamp with R

How to use the R programming language for data science, machine learning, and data visualization. This R programming language tutorial is a comprehensive course that is almost 18 hours in length. It covers everything R programming related. You will learn how to use R to handle csv, excel, SQL files or web scraping. This R programming course will teach you how to use R for Data Science and Data Analysis. This R video course will teach you Machine Learning. Some of the Machine Learning topics you will learn include Linear Regression, K Nearest Neighbors, K Means Clustering, Decision Trees, Random Forests, etc. Learn Machine Learning from the best Machine Learning course in 2023.

This course will teach you how to program with R, create amazing data visualizations, and use machine learning with R! Here are some of the topics we’ll learn:

Programming with R
Advanced R features
Using R data frames to solve complex tasks
Use R to manage Excel files
Web scraping with R
Connect R to SQL
Use ggplot2 for data visualizations
Use plot for interactive visualizations
Machine learning with R, including:
Linear regression
K Nearest neighbors
K means grouping
Decision trees
Random forests
Twitter data mining
Neural networks and deep learning
Support Vectore machines

Introduction to Machine Learning for Data Science

In this introductory course, the “Backyard Data Scientist” will guide you through the wilderness of Machine Learning for Data Science. Accessible to all, this introductory course explains not only Machine Learning, but also where it fits in the “tech sphere around us”, why it is important now and how it will radically change our world today and for them. days to come.

Our exotic journey will include the fundamental concepts of:

The definition of computer train wreck and the one that will make sense.

A data explanation that will make you see data everywhere you look!

One of the “biggest lies” ever sold about future computing.

A real explanation of Big Data, and how to avoid falling into the hype.

What is artificial intelligence? Can a computer really think? How do computers do things like navigate like a GPS or play games anyway?

What is machine learning? What if a computer can think – can it learn?

What is data science and how it relates to magical unicorns!

How IT, Artificial Intelligence, Machine Learning, Big Data, and Data Science interact.

To make sense of the Machine part of Machine Learning, we’ll explore the Machine Learning process:

How do you solve machine learning issues and what are the five things you need to do to be successful?

How to ask the right question, to be solved by Machine Learning.

Identify, obtain and prepare the right data… and manage the dirty data!

How each mess is “unique” but ordered data is like families!

How to identify and apply machine learning algorithms, with exotic names such as “Decision Trees”, “Neural Networks”, “K’s Closest Neighbors” and “Naive Bayesian Classifiers”

And the biggest pitfalls to avoid and how to tune your machine learning models to ensure a successful outcome for data science.

You will learn:
Really understand what computing, algorithms, programming, data, big data, artificial intelligence, machine learning and data science are.
Understand how these different areas fit together, how they are different and how to avoid marketing fluff.
The impacts of machine learning and data science on society.
To truly understand computer technology has changed the world, with an appreciation of scale.
Find out what problems machine learning can solve and how the machine learning process works.
How to avoid problems with Machine Learning, to implement it successfully without losing your mind!

Introduction to Machine Learning in Production

By Andrew Ng. In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.

Best Machine Learning books 2023

The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Edition

The Elements of Statistical Learning: Data Mining, Inference, and Prediction Second Edition by Trevor Hastie, Robert Tibshirani and Jerome Friedman describes important ideas in a variety of fields such as medicine, biology, finance, and marketing within a common conceptual framework. Although the focus is statistical, the emphasis is on concepts rather than mathematics. Many practical examples are given, with generous use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The coverage of the book is extensive, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification and reinforcement trees, the first comprehensive treatment of this topic in a book.

This important new edition introduces many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and loop path algorithms, non-negative matrix factoring, and spectral clustering. There is also a chapter on “big” data methods (p greater than n), which includes multiple tests and false discovery rates.

Deep Learning (Adaptive Computation and Machine Learning series)

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville offers mathematical and conceptual training, covering relevant concepts in linear algebra, probability theory and information theory, numerical calculus, and machine learning. Describes deep learning techniques used by industry professionals, including deep feedback networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and studies applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, the book offers research perspectives, covering theoretical topics such as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

The Hundred-Page Machine Learning Book

The Hundred Page Machine Learning Book by Andriy Burkov is the most comprehensive applied AI book available. It’s packed with best practices and design patterns for creating scalable and reliable machine learning solutions. Andriy Burkov has a PhD. in AI and leads a machine learning team at Gartner. This book draws on Andriy’s 15 years of experience in solving problems with AI, as well as the published experience of industry leaders.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning by Christopher M. Bishop on pattern recognition to present the Bayesian point of view. The book presents approximate inference algorithms that allow fast approximate answers in situations where exact answers are not feasible. Use graphical models to describe probability distributions when no other book applies graphical models to machine learning. No prior knowledge of pattern recognition or machine learning concepts is assumed. Knowledge of multivariate calculus and basic linear algebra is required, and some experience using probability would be helpful but not essential, as the book includes a separate introduction to basic probability theory.

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 3rd Edition

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 by Sebastian Raschka and Vahid Mirjalili is a comprehensive guide to machine learning and deep learning with Python. It acts as a step-by-step tutorial and a reference you will come back to when building your machine learning systems.

Updated for TensorFlow 2.0, this new 3rd edition introduces readers to its new features in the Keras API, as well as the latest additions to scikit-learn. It has also been expanded to cover advanced reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, which helps you learn to use machine learning algorithms to classify documents.

What you are going to learn
Mastering the frameworks, models, and techniques that allow machines to “ learn ” from data
Use scikit-learn for machine learning and TensorFlow for deep learning
Apply machine learning to image classification, sentiment analysis, smart web apps, and more.
Create and train neural networks, GAN, and other models.
Discover best practices for evaluating and fitting models
Predict ongoing target outcomes using regression analysis
Dig deep into text and social data using sentiment analysis

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands On Machine Learning with Scikit Learn Using concrete examples, a minimal theory, and two production-ready Python frameworks – Psychite-Learn and TenserFlow by Aurélien Géron helps you gain an intuitive knowledge of the concepts and tools of creating intelligent systems. You will learn a variety of machine learning techniques, including general linear regression and advances in deep neural networks. A programming experience is just what you need to get started with the practice in each chapter to help you apply what you have learned.

Explore machine learning landscapes, especially neural networks
Use Psychit-Learn to follow the example of the end-to-end machine learning project
Look for several training models including support vector machines, decision trees, random forests and aggregation methods
Use the TensorFlow Library to create and train neural networks
Immerse yourself in neural network architectures, including convincing networks, repeat networks, and deep reinforcement learning.
Learn deep neural networks training and scaling techniques

Building Machine Learning Powered Applications: Going from Idea to Product

Learn the skills to design, build, and deploy applications based on machine learning (ML). During this practical manual, you will create an example of an application controlled by ML from the initial concept to the deployed product. Data scientists, software engineers and product managers, including experienced scientists and beginners, will learn step-by-step the tools, best practices and challenges involved in creating real ML applications.

Author Emanuel Ameisen, an experienced information scientist who has led an AI training program, is presenting practical ML ideas using code snippets, images, screenshots and interviews with industry leaders. Part I taught you how to plan ML applications and measure success. The second part explains how to create a functional ML model. The third part shows how the model can be improved until it meets your original vision. The fourth section covers deployment and monitoring techniques. You are about to learn:

Set your product goals and set up a machine learning problem
Quickly build the last pipeline from your first end and acquire an initial data set
Train and evaluate your ML models and address performance barriers
Place and monitor your model in a manufacturing environment

Introduction to Machine Learning with Python: A Guide for Data Scientists

Machine learning has become an integral part of many commercial applications and research projects, but the region is not exclusive to large companies with large research teams. If you use Python, even as a beginner, this book will teach you practical ways to create your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

Learn the steps needed to build a successful machine learning application, including Python and the Psychite-Learn Library. Authors Andreas Mueller and Sarah Guido focus on the practicality of using machine learning algorithms rather than the math behind them. Getting acquainted (gain, obtain) with present-day libraries will help you to get the most out of this book. You will learn:

Basic concepts and applications of machine learning
Advantages and disadvantages of widely used machine learning algorithms
What aspects of data should be focused on, including how to present data processed by machine learning?
Advanced methods for model evaluation and parameter adjustment
The concept of pipelines in chain models and equipping your workflow
Methods of working with text data, including text-specific processing techniques
Advice for improving your machine learning and data science skills

Machine Learning For Absolute Beginners: A Plain English Introduction

The second edition of Machine Learning for Absolute Beginners was created and designed for the perfect beginner. This means explanation in simple English and no coding experience required. When the basic algorithm is introduced, clear explanations and visual examples are added to make the house clear to follow and interesting.

This new edition introduces a number of issues, including cross-validation, data cleansing, and overall modeling. Please note that this book is not a continuation of the first edition, but a revised and revised version of the first edition. Readers of the first edition should not feel pressured to buy this second edition. In this step-by-step guide, you will learn:

– How to download free datasets
– The machine learning tools and library you need
– Data cleanup strategies including hot encoding, integration and missing data processing
– Prepare data for analysis with K-fold validity
– Regression analysis to create trend lines
– Clustering with K-average and nearest K-neighbor
– The basics of neural networks
– Bias / variant to improve your machine learning model
– Decide to decode the tree
– How to create your first machine learning model to predict room quality using Python

Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition

Updated and revised the second edition of the best-selling guide to explore and master the most important algorithms for solving complex machine learning problems. Updated to include new algorithms and techniques. The code has been updated to Python 3.8 and TensorFlow 2.x new coverage of regression analysis, time series analysis, deep learning models and advanced applications.

The second version, the Mastering Machine Learning Algorithm, helps you use the true power of machine learning algorithms to implement clever ways to meet today’s irresistible data needs. This recently updated and revised guide will help you master the algorithms widely used in semi-supervised learning, empowerment learning, supervised learning and observational learning.

You will use all the modern libraries of the Python ecosystem, including Numpy and Keras, to extract functionality from various data complexities. Dyeing from the Bayesian models to the hidden Markov models from the Monte Carlo Markov chain algorithm, this machine learning book teaches you how to extract entities from your dataset, reduce complex dimensions, and create models. Supervise and semi-supervise using Python based models from libraries such as Psychit-Learn. You will learn complex techniques such as the highest probability estimates, Hibbian learning and the formation of efficient neural networks for complete learning. You will also discover practical applications for how to use X. .

Towards the end of this book, you will be able to apply end-to-end machine learning problems and use ready-to-solve and case scenarios. You will learn:

Understand the features of a machine learning algorithm
Apply algorithms from supervised, semi-supervised, monitored and RL domains
Learn how regression works in time series analysis and risk forecasting
Create, model and train complex potential models
Collect big data and evaluate the accuracy of the model
Discover how artificial neural networks work – train, adapt and validate them
Work with automatic encoders, Hebrew networks and GNS

TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.

Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary.

Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures
Work with Arduino and ultra-low-power microcontrollers
Learn the essentials of ML and how to train your own models
Train models to understand audio, image, and accelerometer data
Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML
Debug applications and provide safeguards for privacy and security
Optimize latency, energy usage, and model and binary size

Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning From Scratch)

Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.

This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition.

In this step-by-step guide you will learn:

• How to download free datasets
• What tools and machine learning libraries you need
• Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data
• Preparing data for analysis, including k-fold Validation
• Regression analysis to create trend lines
• Clustering, including k-Means Clustering to find new relationships
• The basics of Neural Networks
• Bias/Variance to improve your machine learning model
• Decision Trees to decode classification
• How to build your first Machine Learning Model to predict house values using Python

Machine Learning For Dummies 2nd Edition

by John Paul Mueller. While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android―as in the movie Ex Machina―it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models―and way, way more.

Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn’t assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying―and fascinating―math principles that power machine learning but also shows that you don’t need to be a math whiz to build fun new tools and apply them to your work and study.

Understand the history of AI and machine learning
Work with Python 3.8 and TensorFlow 2.x (and R as a download)
Build and test your own models
Use the latest datasets, rather than the worn out data found in other books
Apply machine learning to real problems
Whether you want to learn for college or to enhance your business or career performance, this friendly beginner’s guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that’s impacting lives for the better all over the world.

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

by Valliappa Lakshmanan, Sara Robinson, Michael Munn. The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.

In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.

You’ll learn how to:

Identify and mitigate common challenges when training, evaluating, and deploying ML models
Represent data for different ML model types, including embeddings, feature crosses, and more
Choose the right model type for specific problems
Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning
Deploy scalable ML systems that you can retrain and update to reflect new data
Interpret model predictions for stakeholders and ensure models are treating users fairly