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Top Machine Learning Books to read in 2021
Here’s the top ML books at every level.
Machine Learning is on the raise with big businesses implementing AI in every single business domain.
If you want to jump in, or you’re already in trying to learn more, here’s the list of top machine learning books you can find in 2021. Whether you’re an absolute beginner and haven’t heard of Python before (it’s not a snake!), or you’ve dozens of CNNs in PyTorch, you’ll find something for you. Read on.
ML Book for Absolute Beginners
If you want to start with Machine Learning, then you should read this book:
Introduction to Machine Learning with Python is a gentle introduction to all the essential tasks required to execute machine learning. It does not need previous knowledge of Python or any knowledge about machine learning.
Instead, it incorporates basic principles in Python and addresses different approaches in Python. That is the best book I’ve ever seen for a Machine Learning Engineer’s entry-level role.
Books for Intermediate Learners
If you’ve worked already on some machine learning projects and grew to like machine learning modeling, here are a couple of books that will help you deepen your knowledge:
The Python Machine Learning book is just an efficient book that includes real code samples based on practical use. It begins with basic AI concepts and concludes with designing sophisticated machine learning models. The book is really simple to understand as it goes through the GANs at a college level and the latest innovations to the sector.
This book is invaluable if you know Python and are involved in deep/neural network learning. This is a perfect guide for discovering how to proceed from scratch with machine learning or going over what you already learned.
Written for engineers and data scientists who want to build practical machine learning and deep learning technology, this book is perfect for anybody who wants to teach machines how to learn from data.
There have been several changes in this current third version, including an introduction to Keras and the newest updates to scikit-learn. The appendix also provides a comprehensive reference to the studies relevant to reinforcement learning and the numerous new approaches that have been built by the use of generative adversarial networks.
Finally, this book discusses a subfield of natural language processing (NLP) named sentiment analysis, letting you understand how to use machine learning algorithms to identify documents.
Hands-on Machine Learning with Scikit-Learn and TensorFlow (2nd edition) is also an excellent purchase at Amazon. It is a bit out of date, but a good read. The book touches both fields (classification approaches, dimensionality reduction, and neural networks and deep learning) and then dives further into neural networks and deep learning.
Pattern Recognition and Machine Learning is a book that runs over simple algorithms beginning with a statistics revision. This book is mainly concerned with literary elements of Machine Learning, and it is a perfect accompaniment to other, more realistic works.
ML Books for Experts
Your reading should be adjusted to focus on current research at a more expert rate. Machine learning has come significantly at this moment.
The book “Deep Learning with Python” was written by a Keras developer, one of Python’s most famous machine learning libraries. Deep learning is mostly used in problems where you don’t know an algorithm’s performance and need to find an algorithm with high precision yet is as limited as possible. It is a must-read for profound learning practitioners.
Deep Learning can be a perfect starting point for solving a new challenge in deep learning. It doesn’t have much code but has exciting ideas on addressing machine learning problems: published by proslover in artificial neural networks and pattern recognition. It includes all the methods in common usage.
This book discusses game theory’s roots, how it applies to conventional game theory and the benefits of the new “mixed-integer linear programming” approach to optimization technologies. The book explains deep learning methods utilized by professionals in the industry.
It describes optimization algorithms, regularization, convolutional networks, pattern simulation, and functional methodology. The book discusses technologies like natural language processing, voice recognition, online recommendation systems, computer vision, bioinformatics, and videogames.
The study offered is essential for their viewpoints on subjects, such as those linked to autoencoders, linear factor models, generalized linear models, weighted generative models, Monte Carlo approaches, the partition mechanism, approximate inference, and deep generative models.
If you are more math-oriented and prefer Probabilistic Mathematics, you’ll enjoy Machine Learning: A Probabilistic Viewpoint. Take a trip through mathematics at the origin of all machine learning strategies. A superficial reading of this would not only offer you a limited amount of in-depth information but also find out far more if you keep reading.
Do you want to be a successful data scientist?
Finally, if you are searching for an explanation of what it takes to become a data scientist, look at my book, the book “Data Science Job: How to become a Data Scientist,” which will lead you through the process.
That is it! May your machine learning journey be full of progress, and I wish you all the best on that journey.
If you have qualified for your very first data science position and are hunting for your first entry-level job, you can sign up for my Data Science Job Course. I teach you precisely what to do and what junior-level data science or machine learning systematics is, and how you will become that within the week.
If you’re looking for other lists of books to read, see:
