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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