Data Science and Machine Learning

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 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)


Supervised Learning


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.

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. 



There are many ways to learnHow to Apply

  • 1


  • 2


  • 3

    Get started now

error: Content is protected !!