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.
This course is targeted at experienced Python developers/statisticians keen to rapidly leverage Machine Learning techniques with hands-on examples. Experience in Python programming and statistics is assumed.
Get to grips with supervised and unsupervised Machine Learning by working with hands-on examples.
Implement Machine Learning solutions in Scikit-Learn and Python step by step.
Overcome real-world drawbacks such as overfitting and produce stable, generalizable, and effective solutions.