Course Title: Data Science and Machine Learning
Duration: 80 Hours
Course Outline
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
Numpy Arrays, Indexing, Slicing, Iterating, Stacking and Splitting, Numpy Operations.
Pandas
Introduction, Series, Data Frames, Merging, Joining and Concatenating, Operations, Data i/p and o/p.
Matplotlib
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)
Supervised Learning
Regression
Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Ridge and Lasso Regression, Model and Performance Evaluation.
Classification
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.
Unsupervised Learning
Clustering, K-Means Clustering, Hierarchical Clustering, Principal Component Analysis
Ensemble Techniques
Max Voting, Averaging, Weighted Average, Stacking, Blending, Bagging & Boosting, AdaBoost, Gradient Boosting, XGBoost, CatBoost and Light GBM.
Association Rule Learning
Apriori, Eclat.
Reinforcement Learning
Intelligent Agents, Markov Decision Processes, Dynamic Programming, Monte Carlo, Approximation Methods
Recommender Systems(RS)
Popularity Based RS, Content-Based Filtering, Collaborative Filtering, Hybrid Approaches.
Deployment Strategies
Model Pipelines and Applications, Model Architecture, API Development and CI/CD concepts, Paas and AWS deployment.
