Machine Learning Bootcamp - Beginner
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About this course
Contributors & Instructors
What you will learn?
Course Content
1. Guidelines
1
Guidelines
2. Module 0: Python Fundamentals for Data Science
3
Kick-Off Event
Presentation - Kick-Off Event
Python Fundamentals for Data Science
3. Module 1: Data Analysis, Visualization and Statistics
10
Day 1: Data Analytics, Pandas and Working with CSV files
Day 1 Tutorial: Diving Deep into Pandas
Day 1: Exercise Solutions
Day 1 Self-Practice
Day 1 Live Session: Introduction to Machine Learning & Fundamentals of Python
Day 2: Scatter Plot, Outliers and Correlation
Day 2 Tutorial: Data Visualization & Diving Deep into Matplotlib
Day 2: Additional Resources
Day 3: Statistics
Day 3: Linear Algebra and Matrices
4. Module 2: Data Preparation
9
Day 4: Introduction to Machine Learning Fundamentals, One Hot Encoding and Class Imbalance
Day 4 Tutorial: Data Pre-processing - Handling missing values and dealing with class imbalance
Day 4: Tutorial Slides
Day 4 Session: Data Preparation 101 for Machine Learning Model Building
Day 4: Instructor's Slides
Day 4 Notebook: Data Preparation 101
Day 5 Tutorial: Data Preprocessing & Exploratory Data Analysis
Day 5: Tutorial Slides
Day 5: Data Cleaning Practice
5. Module 3: Model Building
19
Day 6: Machine Learning Categorization
Day 6: Introduction to Linear Regression
Day 6: Simple Linear Regression
Day 7 Tutorial: Introduction to Linear Regression & Types of Machine Learning Models
Day 7: Multiple Linear Regression
Day 7: Evaluating a Regression Model
[Optional] Day 7: Gradient Descent
Day 7: Decision Tree Regressor
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: Regression Forest
Day 8: Bias and Variance
Day 9: Introduction to Logistic Regression
Day 9: Multiclass Logistic Regression
Day 9 Tutorial: Hands-on Session on Logistics Regression
Day 9: Evaluating the performance of a Classification Model
Day 10: Decision Tree for Classification
Day 10: Random Forest
6. Module 4: Model Optimization and Hyperparameter Tuning
6
Day 11 Session: Optimizing Machine Learning Models & Model Evaluation Metrics
Day 11: Speaker Slides
Day 11: Notebook
Day 12 Session: End to End Machine Learning Model Building
Day 12: Speaker Slides
Day 12: Session Notebook
7. Module 5: Feature Selection
5
Day 13 Tutorial: Introduction to Feature Importance and Feature Selection in Machine Learning
Day 13: Feature Selection
Day 13: Feature Selection Notebook
Session: Machine Learning Problem Solving
Session Resources
8. [Optional] Module 6: Ensemble Models
2
Day 14: Introduction to Ensemble Models
Day 15: An overview of Boosting Algorithms
9. Assignments
6
Assignment Schedule and Instructions
Assignment 1: Reference Material
Assignment 2: Datathon
Assignment 2: Instructions for quiz
Assignment 3
Assignment 3: Instructions for Quiz
