Intellipaat Machine Learning with Python training is a comprehensive course for mastering various aspects of Machine Learning. You will learn about Machine Learning with Python programming, supervised and unsupervised learning, Support Vector Machines, Random Forest Classifiers, best practices of Machine Learning and more through hands-on projects and case studies. This Machine Learning Course will give you in-depth knowledge about the technology.

What will you learn in this Machine Learning course online training?

• Fundamentals of using data to train machines
• Representation of an artificial neural network
• Linear regression with multiple variables using Python
• Logistic regression for classifying data using Python
• Support Vector Machines algorithms
• Designing of a Machine Learning system
• Principle Component Analysis for data modeling

Machine Learning Projects

What projects I will be working in this Machine Learning certification course?

Project 1: Customer Churn Classification

Topics: This is a real-world project that gives you hands-on experience in working with most of the machine learning algorithms. The main components of the project include the following:

• Manipulating data to extract meaningful insights
• Visualizing data to find patterns among different factors
• Implementing these algorithms – linear regression, decision tree, naïve Bayes

Learn more from this Machine Learning Tutorial

Project 2: Recommendation for Movie, Summary

Topics: This is a real-world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details, and others. The main components of the project include the following:

• Recommendation for movie
• Two Types of Predictions – Rating Prediction, Item Prediction
• Important Approaches: Memory-Based and Model-Based
• Knowing User-Based Methods in K-Nearest Neighbor
• Understanding Item Based Method
• Matrix Factorization
• Decomposition of Singular Value
• Data Science Project discussion
• Collaboration Filtering
• Business Variables Overview