A Machine Learning course from Zero to Hero should cover fundamental concepts, algorithms, and practical applications. Below is a structured roadmap:

1. Introduction to Machine Learning

What is Machine Learning?

Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

Real-world Applications of ML

ML vs Traditional Programming

2. Mathematics for Machine Learning

Linear Algebra

Vectors, Matrices, and Tensors

Matrix Operations (Transpose, Inverse, Determinants)

Eigenvalues and Eigenvectors

Dot Product and Norms

Probability & Statistics

Probability Theory (Bayes’ Theorem, Conditional Probability)

Distributions (Gaussian, Bernoulli, Binomial, Poisson)

Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)

Hypothesis Testing and Confidence Intervals

Calculus

Derivatives and Integrals

Partial Derivatives & Gradient Descent

Chain Rule and Backpropagation

3. Python for Machine Learning

Python Basics (Lists, Dictionaries, Functions, Loops)

NumPy (Arrays, Vectorized Computation)

Pandas (DataFrames, Data Manipulation)

Matplotlib & Seaborn (Data Visualization)

Scikit-learn Basics

4. Data Preprocessing & Feature Engineering

Handling Missing Values

Encoding Categorical Variables (One-Hot Encoding, Label Encoding)

Feature Scaling (Normalization, Standardization)

Handling Outliers

Feature Selection Techniques

Feature Extraction (PCA, LDA)

5. Supervised Learning

Regression Algorithms

Linear Regression

Polynomial Regression

Ridge & Lasso Regression

Multiple Regression

Classification Algorithms

Logistic Regression

Decision Trees

Random Forest

Support Vector Machines (SVM)

K-Nearest Neighbors (KNN)

Naïve Bayes Classifier

6. Model Evaluation & Optimization

Train-Test Split & Cross-Validation

Evaluation Metrics (Accuracy, Precision, Recall, F1-score, AUC-ROC)

Overfitting & Underfitting

Bias-Variance Tradeoff

Hyperparameter Tuning (Grid Search, Random Search, Bayesian Optimization)

7. Unsupervised Learning

Clustering

K-Means Clustering

Hierarchical Clustering

DBSCAN

Gaussian Mixture Models (GMM)

Dimensionality Reduction

Principal Component Analysis (PCA)

t-SNE & UMAP

Autoencoders for Dimensionality Reduction

8. Neural Networks & Deep Learning

Fundamentals of Neural Networks

Artificial Neurons & Activation Functions

Feedforward Neural Networks

Backpropagation Algorithm

Loss Functions (MSE, Cross-Entropy)

Gradient Descent Variants (SGD, Adam, RMSprop)

Deep Learning Architectures

Convolutional Neural Networks (CNNs) – Image Processing

Recurrent Neural Networks (RNNs) – Time Series & NLP

Long Short-Term Memory (LSTMs) & GRUs

Transformers & Self-Attention Mechanisms (BERT, GPT)

9. Reinforcement Learning (Optional)

Markov Decision Processes (MDPs)

Q-Learning & Deep Q-Networks (DQN)

Policy Gradient Methods

10. Advanced Topics

Transfer Learning

Generative Adversarial Networks (GANs)

AutoML & Neural Architecture Search (NAS)

Explainable AI (XAI)

Edge AI & On-Device ML (TensorFlow Lite, ONNX)

11. Machine Learning in Production

Model Deployment (Flask, FastAPI, Streamlit)

ML Pipelines (MLflow, Kubeflow)

Model Monitoring & Performance Tracking

Cloud ML Services (AWS SageMaker, Google Vertex AI)

12. Projects & Case Studies

Image Classification using CNNs

Sentiment Analysis using NLP

Predictive Analytics for Stock Prices

Fraud Detection using ML

Chatbot Development