Course Title: Data Science and Machine Learning
Duration: 80 Hours
Module 1: Python Programming (6 Projects)
Basics of Programming
Variables, Strings and Numbers, Math Operators, Built-in functions, List and its methods, Tuples, Dictionaries, User Inputs, Conditionals, Custom Functions, Loops.
Numpy Arrays, Indexing, Slicing, Iterating, Stacking and Splitting, Numpy Operations.
Introduction, Series, Data Frames, Merging, Joining and Concatenating, Operations, Data i/p and o/p.
Data Visualisation, Bar Plot, Pair Plot, Histogram and Box-plot, Distribution Plots, Grids and Matrices.
Module 2: Statistics (3 Projects)
Probability, Combinations, Bayesian Inference, Distributions, Descriptive Statistics, Inferential Statistics, Hypothesis Testing, Advanced Statistical Methods.
Module 3: Machine Learning (10 Projects)
Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Ridge and Lasso Regression, Model and Performance Evaluation.
Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), Kernel SVM, Naïve Bayes, Decision Tree, Random Forest , Model and Performance Evaluation.
Clustering, K-Means Clustering, Hierarchical Clustering, Principal Component Analysis
Max Voting, Averaging, Weighted Average, Stacking, Blending, Bagging & Boosting, AdaBoost, Gradient Boosting, XGBoost, CatBoost and Light GBM.
Association Rule Learning
Intelligent Agents, Markov Decision Processes, Dynamic Programming, Monte Carlo, Approximation Methods
Popularity Based RS, Content-Based Filtering, Collaborative Filtering, Hybrid Approaches.
Model Pipelines and Applications, Model Architecture, API Development and CI/CD concepts, Paas and AWS deployment.