Machine learning is a branch of AI and computer science that focuses on the use of data to imitate the way humans learn and improve its accuracy.
The course is divided into two parts. The first part starts with a brief history of how machine learning started and introduces you to the basics of statistical learning. You will also understand linear regression and classification, which is the logistic regression model. Understand what cross-validation, sampling, and Bootstrap are. Explore how to go beyond linearity; we will specifically look at a couple of interesting examples to improve the linear regression model to see if we can create models that are non-linear.
The second part of the course is completely hands-on labs, which start with an example of predicting fuel efficiency in linear regression. We will then look at a lab on logistic regression with a little bit of mathematics behind it. Understand another lab session on random forests and do a review of decision trees as well. Next, we will look at a lab session on Eigenfaces by using Principle Component Analysis (PCA) and wrap up a course with a lab on ROC-AUC (Receiver Operating Characteristic Curve-Area Under Curve).
By the end of the course, you would have given yourself the skills and confidence to start programming machine learning algorithms.
All resources and code files are placed here: https://github.com/PacktPublishing/Fundamentals-of-Machine-Learning
Learn the basics of statistical learning
Understand linear regression, classification, and supervised learning
Understand sampling and Bootstrap in machine learning
Explore model selection and regularization
Understand random forests and decision trees
Explore labs on Multilayer Perceptron (MLP)?and RNN
Each lab session covers one single topic, which will ensure that the topics covered in the course are well understood.