MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, Stereo | Duration: 2h 14m | 452 MB

Genre: eLearning | Language: English

Get hands-on with machine learning using Python.

Given the constantly increasing amounts of data they're faced with, programmers have to come up with better solutions to make machines smarter and reduce manual work. In this Machine Learning course, you'll use Python to craft better solutions and process them effectively.

We start by focusing on key ML algorithms and how they can be trained for classification and regression. We will also work with Supervised and Unsupervised learning to help to get to grips with both types of algorithm. We will use the highly popular Scikit-learn library throughout the course while performing various ML tasks.

By the end of the course, you will be adept at using the concepts and algorithms involved in Machine Learning. This is a highly practical course and will equip you with sufficient hands-on training to help you implement ML skills right after finishing the course.

All the code and supporting files for this course are available on Github at

https://github.com/PacktPublishing/Learn-Machine-Learning-in-3-Hours

Style and Approach

This course consists of a series of worked example problems; for each worked example problem, we make use of different supervised and unsupervised Machine Learning algorithms. We also look at some smaller one-video worked examples to define a series of fundamental concepts which are crucial for reliably deploying stable Machine Learning systems in the real world.

What You Will Learn

How Machine Learning algorithms fit data.

Using PCA (Principal Component Analysis) to explore and visualize data easily.

Implementing Unsupervised K-Means clustering.

Leveraging the power of Unsupervised K-Nearest-Neighbor clustering.

Effective implementation of Supervised SVM (Support Vector Machine) fitting

Getting hands-on with Supervised Random Forest Fitting

Implementing Supervised Gradient Boosting for classification

Hyperparameter fitting and performance-tuning algorithms.

Table of Contents

SETTING UP A MACHINE LEARNING PROJECT IN SCIKIT-LEARN

UNSUPERVISED K-MEANS CLUSTERING IN SCIKIT-LEARN

SUPERVISED K-NEAREST-NEIGHBOR CLASSIFICATION IN SCIKIT-LEARN

SUPERVISED SUPPORT VECTOR MACHINE CLASSIFICATION IN SCIKIT-LEARN

SUPPORT VECTOR MACHINE REGRESSION IN SCIKIT-LEARN

SUPERVISED GRADIENT BOOSTING IN SCIKIT-LEARN