Sign In
Machine Learning with Python

Machine Learning with Python


This course is a beginner-friendly introduction to Machine Learning libraries like Scikit-learn, XGBoost etc. By the end of this course, you will build a classical machine learning project using a real-world dataset.

Lesson 1 - Linear Regression with Scikit Learn

  • Preparing data for machine learning
  • Linear regression with multiple features
  • Generating predictions and evaluating models

Lesson 2 - Logistic Regression for Classification

  • Downloading & processing Kaggle datasets
  • Training a logistic regression model
  • Model evaluation, prediction & persistence

Assignment 1 - Train Your First ML Model

  • Download and prepare a dataset for training
  • Train a linear regression model using sklearn
  • Make predictions and evaluate the model

Lesson 3 - Decision Trees and Hyperparameters

  • Downloading a real-world dataset
  • Preparing a dataset for training
  • Training & interpreting decision trees

Lesson 4 - Random Forests and Ensembling

  • Training and interpreting random forests
  • Ensemble methods and random forests
  • Hyperparameter tuning of random forests

Assignment 2 - Decision Trees and Random Forests

  • Prepare a real-world dataset for training
  • Train decision tree and random forest
  • Tune hyperparameters and regularize

Lesson 5 - Machine Learning Case Study

  • Understand business needs and explore the data
  • Prepare data for modeling and create a baseline
  • Train, evaluate, finetune, and ensemble models

Lesson 6 - Unsupervised Machine Learning

  • Clustering using KMeans and DBSCAN
  • Dimensionality reduction using PCA and t-SNE
  • Collaborative filtering and recommendations

Gradient Boosting with XGBoostoptional

  • Data preprocessing and feature engineering
  • GBMs training, evaluation, and interpretation
  • K-fold cross validation and hyperparameter tuning

Project - Classical Machine Learning

  • Perform data cleaning & feature engineering
  • Training, compare & tune multiple models
  • Document and publish your work online

Deploying a Machine Learning Model

  • Create a Simple Web app using Flask
  • Run the model locally on your machine
  • Publish the Webpage using Render