Machine Learning

Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. machine learning is a type of artificial intelligence that enables self-learning from data and then applies that learning without the need for human intervention.

CURRICULUM

• Module-1 Introduction to Machine Learning
• - Machine Learning Modelling Flow 
• - How to treat Data in ML
• - Types of Machine Learning 
• - Performance Measures 
• - Bias-Variance Trade-Off 
• - Overfitting & Underfitting 

• Module-2 Linear Regression
• - Venture into the machine learning community by learning how one variable can be predicted using several other variables through a housing dataset where you will predict the prices of houses based on various factors.

• Module-3 Advanced Regression
• - Understand generalised regression and different feature selection techniques along with the perils of overfitting and how it can be countered using regularization.

• Module-4 Logistic Regression
• - Learn your first binary classification technique by determining which customers of a telecom operator are likely to churn versus who are not to help the business retain customers.

• Module-5 Naive Bayes
• - Understand the basic building blocks of Naive Bayes and learn how to build an SMS Spam Ham Classifier using Naive Bayes technique.

• Module-6 Module Selection
• - Learn the pros and cons of simple and complex models and the different methods for quantifying model complexity, alongwith regularisation and cross validation.

• Tools Covered
• - Python

• Module-7 Tree Models
• - Learn how the human decision making process can be replicated using a decision tree and other powerful ensemble algorithms.

• Module-8 Boosting
• - Learn how weak learners can be 'boosted' with the help of each other and become strong learners using different boosting algorithms such as Adaboost, GBM, and XGBoost.

• Module-9 Unsupervised learning: Clustering
• - Learn how to group elements into different clusters when you don't have any pre-defined labels to segregate them through K-means clustering, hierarchical clustering, and more.

• Module-10 Unsupervised Learning: Principal Component Analysis
• - Understand important concepts related to dimensionality reduction, the basic idea and the learning algorithm of PCA, and its practical applications on supervised and unsupervised problems.