Diploma in Machine Learning with Python

Are you interested in AI and would like to delve deeper into machine learning? In course, you will learn about the various models of machine learning depending on the objectives to be achieved. Be introduced to multiple logarithms, along with the numerous parametric and non-parametric models. Be a cut above the rest by understanding machine learning with this easy-to-follow course with optional certification!

What You Will Learn In This Free Course

Machine Learning

This module is about machine learning. Following the introduction to machine learning, you will be taught about feature scaling, data cleaning, and feature engineering. Next, linear and logistic regression will be covered, along with the optimization algorithm. Finally, you will also be taught regression and correlation methods.

Key Nearest Neighbours

This module is about Key Nearest Neighbours (KNN). You will learn about the parametric and non-parametric models. First, the exploratory data analysis on the Iris dataset will be covered, along with KNN algorithm implementation. Then we will discuss the decision boundary visualization.

Decision Trees

This module is about decision trees. First, you will learn about entropy and information gain. Then, the implementation of the various steps of the decision tree algorithm will be covered, along with its evaluation. Finally, you will learn how to plot important features and understand decision tree hyper-parameters.

First Course Assessment

This First Course Assessment enables you to review your learning so you can determine your knowledge and understanding of module one, two and three the following course.; Module

Ensemble Learning and Random Forests

This module is about ensemble learning and random forests. You will be taught about bootstrap sampling and bagging. You will learn about the out-of-bag error. The definition of the random forest will be covered, along with hyper-parameters and the pros and cons of random forests.

Support Vector Machines

In this module, you will learn about Support Vector Machines (SVM) and machine learning. SVM Hard and Soft Margin will be covered, along with SVM Kernel Trick and Types. The linearity and regression of the SVM will also be covered, along with data standardization, K-Means algorithm and clusters

Principal Component Analysis

The Principal Component Analysis (PCA) is covered in this module. First, learn about the PCA drawbacks and the PCA algorithm steps. Then, we will address the main applications of PCA, compression and data pre-processing. We will also cover facial recognition and data visualization.

Second Course Assessment

This Second Course Assessment enables you to review your learning so you can determine your knowledge and understanding of module four, five and six the following course.; Module

Course assessment