The Complete Machine Learning Bootcamp: Build, Evaluate,Tune

Master the Fundamentals of Machine Learning: Workflow, Algorithms, and Optimization for Real-World Applications

What you'll learn

Master the end-to-end ML workflow: data prep, model building, and deployment

Understand and apply key ML algorithms, including regression, clustering, and neural networks

Evaluate models with metrics like accuracy, F1-Score, and Silhouette

Optimize models using techniques like hyperparameter tuning and feature selection

Learn to handle data challenges, including missing values, outliers, and feature engineering

Explore ethical considerations and trade-offs like fairness, interpretability, and scalability in ML models

Requirements

Eagerness to learn machine learning—no prior ML experience needed!

Description

Unlock the power of Machine Learning with this beginner-friendly, hands-on course designed to take you from zero to mastery! Whether you're a student, developer, or professional, this course will equip you with the skills to build and optimize machine learning models for real-world applications.You’ll start by understanding the basics of Machine Learning—what it is, how it works, and its real-life applications. From there, you’ll dive into the different types of machine learning: supervised, unsupervised, and reinforcement learning, exploring popular algorithms like linear regression, decision trees, clustering, and more.We’ll guide you through the entire ML workflow: data collection, preprocessing, feature engineering, model evaluation, and deployment. You’ll learn how to handle messy data, select the right algorithms, and optimize your models for the best results.This course also covers essential topics like evaluation metrics, hyperparameter tuning, explainable AI, and ethical considerations, ensuring you develop not just technical skills but also a strong foundation for practical problem-solving.No prior experience with Machine Learning is required—just a basic understanding of Python and a curiosity to learn. By the end, you’ll have the confidence to build your own machine learning models and tackle real-world challenges. Join us and start your ML journey today!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Module 1: Overview of Machine Learning

Lecture 2 Machine learning overview

Lecture 3 Categories of ML and Business applications

Section 3: Forms of Machine learning

Lecture 4 Supervised learning

Lecture 5 Unsupervised learning

Lecture 6 Reinforcement learning

Section 4: Machine learning Workflow

Lecture 7 Understand ML workflow

Lecture 8 Model Generation, Evaluation,Tuning, Deployment and Testing

Lecture 9 Factors involved in selecting ML algorithm

Section 5: Module 4 : Machine learning - Data

Lecture 10 Data preparation and Preprocessing

Lecture 11 Handling Missing Data, Outliers and Categorical Data

Lecture 12 Feature Engineering

Lecture 13 Feature Scaling and Selection techniques

Lecture 14 Data labelling

Lecture 15 Splitting Data (Training, Testing, Validation)

Lecture 16 Challenges with Data Preparation

Lecture 17 Data Quality issues

Section 6: Module 5 : Machine Learning Algorithms

Lecture 18 Overview of Machine Learning Algorithms

Lecture 19 Supervised Learning - Simple and Multiple linear regression

Lecture 20 Logistic Regression

Lecture 21 Tree based models - Decision Tree

Lecture 22 Random Forest

Lecture 23 Ensemble Models - Boosting and Bagging

Lecture 24 Neural Networks

Lecture 25 Unsupervised Learning Algorithms - Clustering K-means

Lecture 26 Clustering: Hierarchical Clustering

Lecture 27 Unsupervised Learning Algorithms - Dimensionality Reduction - PCA

Lecture 28 tSNE (t-Distributed Stochastic Neighbor Embedding)

Lecture 29 Advanced Algorithms: Reinforcement Learning

Lecture 30 Deep Learning and Applications

Lecture 31 Comparison between RL and DL

Section 7: Module 6: Model Evaluation

Lecture 32 Overview and key aspects of Model evaluation

Lecture 33 Overfitting and Underfitting

Lecture 34 Bias -Variance Tradeoff

Lecture 35 Supervised Regression Metrics - Regression Metrics: MAE, MSE

Lecture 36 RMSE and R square

Lecture 37 Classification Metrics: Accuracy, Precision, Recall, F1-Score

Lecture 38 ROC and AUC

Lecture 39 Unsupervised Clustering metrics -Silhouette Score

Lecture 40 Elbow method

Lecture 41 Limitations of ML functional performance metrics

Lecture 42 Selecting ML Functional Performance metrics

Section 8: Module 7: Non-Functional Performance Metrics (Supervised & Unsupervised)

Lecture 43 Accuracy vs. Interpretability

Lecture 44 Scalability and Training Time

Lecture 45 Fairness and Ethical Considerations

Section 9: Module 8: Model Optimization and Tuning

Lecture 46 Deep dive in understanding Model Optimization and Tuning

Lecture 47 Understanding hyperparameter

Lecture 48 Hyperparameter Tuning: Grid Search

Lecture 49 Random search

Lecture 50 Bayesian Optimization

Lecture 51 Techniques for Improving Model Performance: Cross Validation

Lecture 52 Regularization - Lasso and Ridge

Lecture 53 Feature selection

Lecture 54 Ensemble Techniques : Boosting, Bagging and Stacking

Lecture 55 Explainable AI

Lecture 56 SHAP

Lecture 57 LIME

Lecture 58 Use Cases and Benefits of Explainable AI

Lecture 59 Key takeaways

This course is designed for: Aspiring Data Scientists and ML Enthusiasts eager to build expertise from scratch. Developers and Programmers looking to expand their skill set with machine learning. Students and Academics pursuing careers in AI, data science, or analytics. Professionals in Any Field who want to understand how ML can solve real-world problems. Beginners in Machine Learning seeking a structured, hands-on learning path. No prior ML experience is required—just curiosity and a willingness to learn!