Complete Book Of A Practical Mastering ML Implementation Guide to Predictive Data Analytics Using Python

Overview:

This book is very useful for Novice machine learning practitioners interested in understanding advanced topics like hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and the fundamentals of reinforcement learning, This book is jam-packed with knowledge about machine learning techniques and Python programming tools. The foundations of the Python programming language, the development frameworks for systems, and the history and progress of machine learning will all be covered.

The author did a fantastic job of condensing the length of this topic into one chapter while still covering its depth. Important data mining/analysis concepts like feature dimension reduction, regression, and time series forecasting are also taught, as well as how to implement them effectively in Scikit-learn. You’ll finally look at sophisticated text mining. You can experiment with these examples and modify them to your liking by using the iPython notebooks that contain all the code that is described in the book.

The use of advanced neural network, deep learning, and text mining techniques will be your final topic of study. For anyone who wants to get started quickly and create machine learning solutions, this book is incredibly helpful. I hope you will benefit from and find this book useful.

Topics covered by this book:

• In Chapter 1, you will learn how to set up the Python development environment, the main ideas behind Python programming, and a high-level overview of the Python language and its core philosophy.

• In Chapter 2, you will learn about the background, development, and many frameworks used in actual machine learning systems today. This knowledge, in my opinion, is crucial since it will broaden your perspective and serve as a foundation for your future exploration.

• Using two important Python libraries, chapter 3 focuses on various supervised and unsupervised machine learning algorithms.

• You’ll learn about the various dangers that one should be aware of and that one will run into when developing machine learning systems in Chapter 4. You’ll also learn how to tackle them using efficient designs that are customary in the business.

• The high-level text mining process overview, essential ideas, and typical methodologies are covered in chapter 5.

• The fundamental idea of deep learning, its evolution (from the perceptron to the convolution neural network), important applications, and implementation are covered in chapter 6.