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Overview of Machine Learning course

Module 1- Introduction

Session 1- Introduction to Artificial Intelligence

Session 2- Introduction to Data Science

Session 3- Introduction to Big Data

Session 4- Introduction to Big Data Analytics

Session 5- Introduction to Hadoop

Session 6- Introduction to Cloud Computing

Module 2- Introduction to Machine Learning

Session 1- Emergence of Machine Learning

Session 2- Introduction to Data Mining

Session 3- Data Objects and Attribute Types

Session 4- Basic Statistical Measures

Session 5- Univariate, Bivariate, Multivariate Data Analysis

Lab- Understanding Exploratory Data Analysis, Statistics and Univariate, Bivariate, and Multivariate

Module 3- Maths essentials of Machine Learning

Session 1- Need of Mathematics for ML

Session 2- Introduction to Mathematics for ML

Session 3- Introduction to Matrices

Session 4- Introduction to Probability

Session 5- Introduction to Vectors

Module 4- Introduction to Python for Machine Learning

Session 1- Installing Anaconda and Jupyter Notebook

Session 2- Introduction to Python

Session 3- Data Types in Python

Session 4- Operators in Python

Session 5- Conditional Statements in Python

Session 6- Looping Constructs in Python

Session 7- Functions in Python

Session 8- Introduction to Numpy Library

Session 9- Introduction to Pandas Library

Lab- Anaconda Installation

Lab- Python Coding

Lab- Numpy Implementation

Lab- Pandas Implementation

Module 5- Introduction to Pre-Processing of the Data

Session 1- Introduction to Data Preprocessing

Session 2- Preprocessing a Dataset

Lab- Preprocessing Air Quality Dataset

Module 6- Introduction to Data Visualization

Session 1- Introduction to Data Visualization

Session 2- Introduction to Matplotlib Library

Session 3- Performing Data Visualization

Session 4- Introduction to Seaborn Library

Lab- Visualizing MT CARS dataset

Module 7- Supervised Machine Learning: Classification

Session 1- Introduction to Supervised Learning

Session 2- Introduction to Classification

Session 3- Understanding K NEAREST NEIGHBORS Algorithm

Session 4- Understanding SUPPORT VECTOR MACHINE algorithm

Session 5- Understanding NAIVE BAYES algorithm

Session 6- Understanding DECISION TREE Algorithm

Lab- Classification Algorithms on Wine Quality Data

Module 8- Supervised Machine Learning: Regression

Session 1- Introduction to Regression

Session 2- Understanding Logistic Regression

Lab- Regression on Air Quality Data

Module 9- Unsupervised Machine Learning

Session 1- Introduction to Unsupervised Learning

Session 2- Introduction to Clustering

Session 3- Introduction to Hierarchical Clustering

Lab- Clustering on Mall Customers data

Module 10- Model Evaluation

Session 1- Understanding Evaluation Metrices

Module 11- Ensemble techniques and Natural Language Processing

Session 1- Introduction to Ensemble Techniques

Lab- Ensemble Techniques on Credit Card Fraud Detection data

Session 2- Learning Natural Language Processing

Session 3- Understanding concepts of Natural Language Processing

Lab- Learning Web Scrapping and Performing NLP

Module 12- Recommendation Engine

Session 1- Introduction to Recommendation Engines

Session 2- Understanding Recommendation Engine types

Lab- Movie Recommendation Engine

Module 13- Introduction to Association Rules

Session 1- Understanding Association Rules

Module 14- Introduction to Dimensionality Reduction

Session 1- Understanding Dimensionality Reduction

Module 15- Introduction to Deep Learning

Session 1- Introduction to Deep Learning

Session 2- Deep Learning Concepts

Session 3- Deep Neural Networks

Lab- Learning Deep Learning on varied data

Resources (Reference files used in lectures)

Congratulations!

Machine Learning Conclusion

Machine Learning project

Machine Learning Project

pd_speech_features

Maching Learning Final exam

Preview - Machine Learning