Machine Learning Bootcamp
Refresh your Machine Learning knowledge and learn exciting concepts like Explainable AI and Model Deployment from industry experts for free
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About this course
Contributors & Instructors
What you will learn?
Course Content
1. Module 0: Python Fundamentals for Data Science
1
Python Fundamentals for Data Science
2. Module 1: Data Analysis, Visualization and Statistics Recap
12
Data Analytics, Pandas and Working with CSV files
Session: Diving Deep into Pandas
Session Exercise Solutions
Self-Practice
Session: Introduction to Machine Learning & Fundamentals of Python
Session Slides
Scatter Plot, Outliers and Correlation
Session: Data Visualization & Diving Deep into Matplotlib
Session Slides
Additional Resources
Statistics
Linear Algebra and Matrices
3. Module 2: Data Preparation
9
Introduction to Machine Learning Fundamentals, One Hot Encoding and Class Imbalance
Session: Data Pre-processing - Handling missing values and dealing with class imbalance
Session Slides
Session: Data Preparation 101 for Machine Learning Model Building
Session Slides
Session Notebook: Data Preparation 101
Session: Data Preprocessing & Exploratory Data Analysis
Session Slides
Data Cleaning Practice
4. Module 3: ML Algorithms and Evaluation Metrics
24
Machine Learning Categorization
Introduction to Linear Regression
Simple Linear Regression
Multiple Linear Regression
Evaluating a Regression Model
Session: Introduction to Linear Regression & Types of Machine Learning Models
Bias and Variance
Decision Tree Regressor
Support Vector Regressor
Regression Forest
Introduction to Logistic Regression
Multiclass Logistic Regression
Hands-on Session on Logistic Regression
Refresher: Input variables, Target variable, Train and Test data intuition
Session: Building your first Classification and Regression Machine Learning Models
Session Notebook
Evaluating a Classification Model
Decision Tree for Classification
Session: Decision Tree Classifier
Support Vector Machine
Naive Bayes Classifier
Random Forest
Ensemble Models
Assignment 1
5. Module 4: Hyperparameter Tuning and Feature Selection
13
Session: Optimizing Machine Learning Models & Model Evaluation Metrics
Session Slides
Session Notebook
Assignment 2
Session: Introduction to Feature Importance and Feature Selection in Machine Learning
Feature Selection
Notebook: Feature Selection
Session: End to End Machine Learning Model Building
Session Slides
Session Notebook
Session: Machine Learning Problem Solving
Session Resources
Assignment 3
6. Module 5: Explainable AI
11
The Importance of Human Interpretable Machine Learning
Model Interpretation Strategies
SHAP
SHAP Implementation
Other Explainable AI Tools
The Future of Interpretability
Session: Importance of Human Interpretable models & Explainable A.I
Session Slides
Session Notebook: SHAP
LIME Implementation
Assignment 4
7. Module 6: Model Deployment
11
Introduction to Model Deployment
STEP 1: Creating a model for deployment
STEP 2: Model serialization and pickling
STEP 3: Creating a Flask Application or API
Session: Deploying Machine Learning Pipelines using PyCaret
Session Resources
STEP 4: Creating a Frontend or UI
Session: Machine Learning Model Deployment 101
Session Slides
STEP 5: Deploying the model with Heroku
Assignment 5
