Machine Learning with Python

Overview

In this course, participants will learn all the concepts of Python and ML along with Supervised and unsupervised learning, understand how Statistical Modeling relates to Machine Learning, and learn to build algorithms with practical hands-on exercises.

Objectives

At the end of Machine Learning with Python training course, participants will

• Learn about the various libraries offered by Python to manipulate, preprocess, and visualize data. | Learn the basics of Machine Learning including an introduction to Supervised and Unsupervised Learning. | Learn to use optimization techniques to find the minimum error in your Machine Learning model. | Learn about K-means Clustering  and Hierarchical Clustering to understand Unsupervised Learning. | Learn to use multiple learning algorithms to obtain better predictive performance. | Understand Neural Networks and apply them to classify image and perform sentiment analysis. | Learn about the various libraries offered by Python to manipulate, preprocess, and visualize data.
• Learn the basics of Machine Learning including an introduction to Supervised and Unsupervised Learning.
• Learn to use optimization techniques to find the minimum error in your Machine Learning model.
• Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers.
• Learn about K-means Clustering  and Hierarchical Clustering to understand Unsupervised Learning.
• Learn to use multiple learning algorithms to obtain better predictive performance.
• Understand Neural Networks and apply them to classify image and perform sentiment analysis.

Prerequisites

Elementary programming knowledge. Familiarity with statistics

Course Outline