✅ Book Reference: Machine Learning with Python Cookbook by Chris Albon

This book is an excellent practical guide for anyone learning or working with machine learning using Python.

It provides hundreds of hands-on recipes that demonstrate how to perform common tasks in data preprocessing, model training, evaluation, and deployment.

Each recipe is concise and solution-focused, showing how to apply popular libraries such as NumPy, pandas, scikit-learn, TensorFlow, and Matplotlib.

It’s particularly useful for students, data enthusiasts, and professionals who want to quickly find code examples for real-world ML problems.

✅ Key Highlights

• Covers data cleaning, feature engineering, and model selection.
• Includes ready-to-use Python snippets for practical implementation.
• Explains core ML concepts with clear and reproducible examples.

This book serves as a handy reference and coding companion throughout your machine learning journey.

A practical reference based on Machine Learning with Python Cookbook by Chris Albon — featuring clear, ready-to-use Python recipes for data preprocessing, model building, and evaluation using libraries like NumPy, pandas, and scikit-learn.

✅ Chapter 1: Working with Vectors, Matrices and Arrays in NumPy

NumPy provides efficient tools for creating, manipulating and performing mathematical operations on vectors, matrices and multidimensional arrays, which form the foundation for numerical computing and data science. The operations in this chapter build essential skills such as indexing, reshaping, applying functions, matrix algebra and statistical computation, enabling faster data processing and optimization compared to traditional Python lists.

Sl. No	Code
1	Creating a Vector
2	Creating a Matrix
3	Creating a Sparse Matrix
4	Pre-allocating Numpy Arrays
5	Selecting Elements
6	Describing a Matrix
7	Applying Functions Over Each Element
8	Finding the Maximum and Minimum Values
9	Calculating the Average, Variance and Standard Deviation
10	Reshaping Arrays
11	Transposing a Vector or Matrix
12	Flattening a Matrix
13	Finding the Rank of a Matrix
14	Getting the Diagonal of a Matrix
15	Calculating the Trace of a Matrix
16	Calculating Dot Products
17	Adding and Subtracting Matrices
18	Multiplying Matrices
19	Inversing a Matrix
20	Generating Random Values

✅ Chapter 2: Loading the Data

This chapter focuses on importing data from various file formats and storage sources such as CSV, JSON, Excel, Parquet, Avro, SQL, cloud buckets, and Google Sheets. It provides practical techniques to load both structured and unstructured datasets for data analytics and machine learning workflows.

Sl. No	Code
1	Loading the Sample Data
2	Creating a Simulated Dataset
3	Loading a CSV File
4	Loading an Excel File
5	Loading a JSON File
6	Loading a Parquet File
7	Loading a avro File
8	Loading a TFRecord File
9	Querying a SQLite Database
9	Querying a Remote SQL Database
9	Loading Data from a Google Sheet
9	Loading Data from an S3 bucket
9	Loading Unstructured Data