Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch

Chapter 1: Getting Started with Python 3 and Jupyter NotebookChapter Goal: Introduce the reader to the basics of Python Programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter Notebook. In the end, we will also have a brief overview of the constituent libraries of sciPy stack.No of pages - 30Sub -Topics1. Introduction to the Python programming language2. History of Python3. Python enhancement proposals (PEPs)4. Philosophy of Python5. Real life applications of Python6. Installing Python on various platforms (Windows and Debian Linux Flavors)7. Python modes (Interactive and Script)8. Pip (pip installs python)9. Introduction to the scientific Python ecosystem10. Overview of Jupyter Notebook11. Installation of Jupyter Notebook12. Running code in Jupyter Notebook Chapter 2: Getting Started with NumPyChapter Goal: Get started with NumPy Ndarrays and the basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy.No of pages: 10Sub - Topics: 1. Introduction to NumPy2. Install NumPy with pip33. Indexing and Slicing of ndarrays4. Properties of ndarrays5. Constants in NumPy6. Datatypes in datatypes Chapter 3: Introduction to Data VisualizationChapter goal - In this chapter, we will discuss the various ndarray creation routines available in NumPy. We will also get started with Visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib.No of pages: 15Sub - Topics: 1. Ones and zeros2. Matrices3. Introduction to Matplotlib4. Running Matplotlib programs in Jupyter Notebook and the script mode5. Numerical ranges and visualizations Chapter 4: Introduction to Pandas Chapter goal - Get started with Pandas data structuresNo of pages: 10Sub - Topics: 1. Install Pandas2. What is Pandas3. Introduction to series4. Introduction to dataframesa) Plain Text Fileb) CSVc) Handling excel filed) NumPy file formate) NumPy CSV file readingf) Matplotlib Cbookg) Read CSVh) Read Exceli) Read JSONj) Picklek) Pandas and webl) Read SQLm) Clipboard Chapter 5: Introduction to Machine Learning with Scikit-LearnChapter goal - Get acquainted with machine learning basics and scikit-Learn libraryNo of pages: 101. What is machine learning, offline and online processes2. Supervised/unsupervised methods3. Overview of scikit learn library, APIs4. Dataset loading, generated datasets Chapter 6: Preparing Data for Machine LearningChapter Goal: Clean, vectorize and transform dataNo of Pages: 151. Type of data variables2. Vectorization3. Normalization4. Processing text and images Chapter 7: Supervised Learning Methods - 1Chapter Goal: Learn and implement classification and regression algorithmsNo of Pages: 301. Regression and classification, multiclass, multilabel classification2. K-nearest neighbors3. Linear regression, understanding parameters4. Logistic regression5. Decision trees Chapter 8: Tuning Supervised L