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Machine Learning Bootcamp

Refresh your Machine Learning knowledge and learn exciting concepts like Explainable AI and Model Deployment from industry experts for free

81 Tutorials
0 Exercises
Advanced Level
100% Online
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About this course

Contributors & Instructors

What you will learn?

Course Content

Python Fundamentals for Data Science

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

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

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

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

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

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

Earn Recognition

certificate

Take the next step towards your Data Science learning journey and make most of the community learning