Python Machine Learning Workbook for Beginners PDF

A practical guide to machine learning with Python through the presentation and guided completion of ten real-world projects

Key Features

• Step-by-step roadmap to data science and machine learning

• A Python crash course in machine learning

• 10 machine learning and data science projects for practical study

Book Description

Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to better understand potential risks. The ever-expanding data, cost-effective data storage, and competitively priced powerful processing continue to drive the growth of ML.

This is the best time you could enter the exciting machine learning universe. Industries are reinventing themselves constantly by developing more advanced data analysis models. These models analyze larger and more complex data than ever while delivering instantaneous and more accurate results on enormous scales.

In this backdrop, it is evident that hands-on practice is everything in machine learning. Tons of theory will amount to nothing if you don’t have enough hands-on practice. Textbooks and online classes mislead you into a false sense of mastery. The easy availability of learning resources tricks you and you become overconfident. But when you try to apply the theoretical concepts you have learned, you realize it’s not that simple.

This is where projects play a crucial role in your learning journey. Projects are doubtless the best investment of your time. You’ll not only enjoy learning but you’ll also make quick progress. And unlike studying boring theoretical concepts, you’ll find that working on projects is easier to stay motivated.

The projects in this book cover ten different interesting topics. Each project will help you refine your ML skills and apply them in the real world. These projects also present you with an opportunity to enrich your portfolio, making it simpler to find a great job, explore interesting career paths, and even negotiate a higher pay package. Overall, this learning-by-doing book will help you accomplish your machine learning career goals faster.

What you will learn

• House price prediction using linear regression

• Filtering spam email messages using Naïve Bayes algorithm

• Predicting used car sale price using Feedforward Artificial Neural Networks

• Predicting stock market trends with RNN (LSTM)

• Language translation using Seq2Seq encoder-decoder LSTM

• Classifying cats and dogs images using Convolutional Neural Networks

• Movie recommender system using item-based collaborative filtering

• Face detection with OpenCV in Python

• Handwritten English character recognition with CNN

• Customer segmentation based on income and spending

Who this book is for

The scripts, images, and graphs are clear and provide visuals to the text description. If you are new to ML and self-study is your only option, then this book is a must.

Author: AI Sciences OÜ