Python Machine Learning for Beginners PDF

This course lays the foundations for both a theoretical and practical understanding of machine learning and artificial intelligence, utilizing Python as a beginner-friendly introduction and invitation to further study

Key Features

• A crash course in Python programming

• Interactive, guided practice through a series of machine learning exercises

• Instant access to PDFs, Python codes, and exercises from the publisher’s website at no extra cost

Book Description

Machine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that’s right. Based on a significant amount of data and evidence, it’s obvious that ML and AI are here to stay.

Consider any industry today. The practical applications of ML are really driving business results. Whether it’s healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing, and sales. You name it. The list goes on. There’s no doubt that ML is going to play a decisive role in every domain in the future.

But what does a machine learning professional do?

A machine learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions.

Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast. You’ll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you’ll learn about machine learning and statistical models for data science.

Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques.

By the end of this course, you will have a firm grasp on the theoretical foundations of machine learning and artificial intelligence as well as having explored and practiced various real-world applications through Python.

What you will learn

• Get up to speed with Python programming

• Explore Python NumPy and Pandas libraries for data analysis

• Practice data visualization via Matplotlib, Seaborn, and Pandas libraries

• Solve regression problems in ML using Sklearn library

• Solve classification problems in ML using Sklearn library

• Study data clustering with ML using Sklearn library

• Cover deep learning with Python TensorFlow 2.0

• Perform dimensionality reduction with PCA and LDA using Sklearn

Who this book is for

This course is specifically designed for those students interested in studying machine learning from its theoretical foundations to advanced applications with Python. No prior experience is required.

Author: AI Sciences OÜ