Back To Module Home

An Introductory Guide to Machine Learning

0% completed

Machine Learning Part-1

Machine Learning and its Types Deep Learning and Recommender Systems What is Regression ?Univariate Linear Regression Multivariate Linear Regression Feature Scaling Linear Regression in Scikit Learn Regularization (Lasso, Ridge, and ElasticNet Regression)Support Vector Regression Nearest Neighbour Regression Decision Tree Regression Feature Engineering and Categorical Variables Encoding Numerical Variables Transformation Feature Selection (Filter Methods)Feature Selection (Wrapper Methods)Feature Selection (Intrinsic Methods)Model Evaluation Measures (Explained Variance Score, MAE, MSE)Model Evaluation Measures (Median Absolute Error, R^2 Score)Dummy Regressors Cross Validation Case Study: House Prices Prediction using Advanced Regression

Machine Learning Part-2

Types of Classification Problems Logistic Regression Support Vector Machines Decision Trees Naive bayes Part-1 Naive bayes Part-2 K-Nearest Neighbors Ensemble Learning Part 1 Ensemble Learning Part 2 XGBoost, Light GBM and CatBoost Learning Curves Model Evaluation Part 1 Model Evaluation Part 2 Dummy Estimators and Handling Imbalance Class Problem Hyper-Parameter Optimization and Kaggle Competition

Machine Learning Part-3

Unsupervised Learning K-Means Clustering Hierarchical Clustering DBSCAN Clustering and Customer Segmentation Apriori Algorithm and Association Rules Principal Component Analysis for Dimensionality Reduction Semi-Supervised Learning Techniques

Machine Learning Tools and Libraries

Automated Machine Learning Pandas Profiling and PyCaret RAPIDS (Using GPU for Fast Computations)

Big Data Tools and Technologies

What is Big Data ?Hadoop Ecosystem Map Reduce Framework Apache Spark and it's Components

Conclusion

...

/

Final Remarks

Final Remarks

Here's a quick summary of what we learned from the "An Introductory Guide to Machine Learning" module.

Congratulations! You have successfully completed the "An Introductory Guide to Machine Learning" module.

Summary

This module was divided into ...