Python Machine Learning Solutions

Machine learning is increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.

With this course, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the course, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modelling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

Style and Approach
These independent videos teach you how to perform various machine learning tasks in different environments. Each of the video in the section will cover a real-life scenario.

Table of Contents

THE REALM OF SUPERVISED LEARNING
CONSTRUCTING A CLASSIFIER
PREDICTIVE MODELING
CLUSTERING WITH UNSUPERVISED LEARNING
BUILDING RECOMMENDATION ENGINES
ANALYZING TEXT DATA
SPEECH RECOGNITION
DISSECTING TIME SERIES AND SEQUENTIAL DATA
IMAGE CONTENT ANALYSIS
BIOMETRIC FACE RECOGNITION
DEEP NEURAL NETWORKS
VISUALIZING DATA