Machine Learning Bootcamp - Advanced

Guidelines

Kick-Off Event

Presentation - Kick-Off Event

[Optional] Python Fundamentals for Data Science

[Optional] Day 1: Data Analytics, Pandas and Working with CSV files

[Optional] Day 1 Tutorial: Diving Deep into Pandas

[Optional] Day 1: Exercise Solutions

[Optional] Day 1 Self-Practice

[Optional] Day 1 Live Session: Introduction to Machine Learning & Fundamentals of Python

[Optional] Day 1 Speaker Slides: Machine Learning and Python Fundamentals

[Optional] Day 2: Scatter Plot, Outliers and Correlation

[Optional] Day 2 Tutorial: Data Visualization & Diving Deep into Matplotlib

[Opyional] Day 2: Tutorial Slides

[Optional] Day 2: Additional Resources

[Optional] Day 3: Statistics

[Optional] Day 3: Linear Algebra and Matrices

[Optional] Day 4: Introduction to Machine Learning Fundamentals, One Hot Encoding and Class Imbalance

[Optional] Day 4 Tutorial: Data Pre-processing - Handling missing values and dealing with class imbalance

[Optional] Day 4: Tutorial Slides

[Optional] Day 4 Session: Data Preparation 101 for Machine Learning Model Building

[Optional] Day 4: Instructor's Slides

[Optional] Day 4 Notebook: Data Preparation 101

[Optional] Day 5 Tutorial: Data Preprocessing & Exploratory Data Analysis

[Optional] Day 5: Tutorial Slides

[Optional] Day 5: Data Cleaning Practice

Day 5: Machine Learning Categorization

Day 6: Introduction to Linear Regression

Day 6: Simple Linear Regression

Day 6: Evaluating a Regression Model

Day 6: Multiple Linear Regression

Day 6 Tutorial: Introduction to Linear Regression & Types of Machine Learning Models

Day 6: Bias and Variance

Day 7: Decision Tree Regressor

Day 7: Support Vector Regressor

Day 7: Introduction to Logistic Regression

Day 7: Regression Forest

Day 7: Multiclass Logistic Regression

Refresher: Input variables, Target variable, Train and Test data intuition

Day 7 Session: Building your first Classification and Regression Machine Learning Models

Day 7: Session Notebook

Day 8: Evaluating the performance of a Classification Model

Day 8: Decision Tree for Classification

Day 8 Tutorial: Decision Tree Classifier

Day 8: Support Vector Machine

Day 8: Naive Bayes Classifier

Day 8: Random Forest

Day 9 Session: Optimizing Machine Learning Models & Model Evaluation Metrics

Day 9: Speaker Slides

Day 9: Notebook

Day 10 Tutorial: Introduction to Feature Importance and Feature Selection in Machine Learning

Day 10: Feature Selection

Day 10: Feature Selection Notebook

Day 11 Session: End to End Machine Learning Model Building

Day 11: Speaker Slides

Day 11: Session Notebook

Session: Machine Learning Problem Solving

Session Resources

Day 11: The Importance of Human Interpretable Machine Learning

Day 11: Model Interpretation Strategies

Day 12: SHAP

Day 12: SHAP Implementation

Day 13: Other Explainable AI Tools

Day 13: The Future of Interpretability

Day 14 Session: Importance of Human Interpretable models & Explainable A.I

Day 14: Speaker Slides

Day 14: Session Notebook - SHAP

[Optional] Day 14: LIME Implementation

Day 15: Introduction to Model Deployment

Day 16: STEP 1 - Creating a model for deployment

Day 17: STEP 2 - Model serialization and pickling

Day 18: STEP 3 - Creating a Flask Application or API

Day 18 Session: Deploying Machine Learning Pipelines using PyCaret

Day 18 Session Resources

Day 19: STEP 4 - Creating a Frontend or UI

Day 19 Session: Machine Learning Model Deployment 101

Day 19 Session Slides

Day 20: STEP 5 - Deploying the model with Heroku

Assignment Schedule and Instructions

Assignment 1: Datathon

Assignment 1: Instructions for Quiz

Assignment 2

Assignment 3