🧠 Machine Learning Mastery

From Mathematical Foundations to Production ML Systems

🎯 What You'll Master

This comprehensive Machine Learning course takes you from mathematical fundamentals to building production-ready ML systems. You'll learn both theory and practice, implementing algorithms from scratch and using industry-standard libraries.

🌱 Beginner Path

Duration: 8-10 weeks

Prerequisites: Python basics, basic math

  • Start with ML fundamentals
  • Learn supervised learning algorithms
  • Build regression & classification projects
  • Master model evaluation

🚀 Intermediate Path

Duration: 6-8 weeks

Prerequisites: ML basics, linear algebra

  • Deep learning & neural networks
  • Advanced algorithms (SVM, XGBoost)
  • Feature engineering & selection
  • Build end-to-end ML pipelines

⚡ Advanced Path

Duration: 4-6 weeks

Prerequisites: Strong ML background

  • MLOps & model deployment
  • AutoML & hyperparameter tuning
  • Time series & reinforcement learning
  • Production ML systems

📚 Complete Curriculum

Part 1: ML Fundamentals & Mathematics

📊

What is Machine Learning?

ML definition, types, applications, and real-world examples

 📐

Mathematics for ML

Linear algebra, calculus, probability & statistics

 🐍

Python ML Libraries

NumPy, Pandas, Scikit-learn, Matplotlib mastery

 🧹

Data Preprocessing

Cleaning, normalization, encoding, feature scaling

Part 2: Supervised Learning

📈

Linear Regression

Simple & multiple regression, gradient descent

 🎯

Logistic Regression

Binary & multiclass classification, decision boundaries

 🌳

Decision Trees & Random Forests

Tree-based models, ensemble methods, feature importance

 🎲

Support Vector Machines

Linear & kernel SVM, margin optimization

 📊

Naive Bayes

Probabilistic classification, text classification

 🎯

K-Nearest Neighbors

Distance metrics, KNN regression & classification

Part 3: Unsupervised Learning

🔵

Clustering Algorithms

K-Means, DBSCAN, hierarchical clustering

 📉

Dimensionality Reduction

PCA, t-SNE, feature selection techniques

 ⚠️

Anomaly Detection

Outlier detection, isolation forests, autoencoders

 🔗

Association Rule Learning

Apriori algorithm, market basket analysis

Part 4: Deep Learning

🧠

Neural Networks

Perceptron, backpropagation, activation functions

 🖼️

Convolutional Neural Networks

CNN architecture, image classification, transfer learning

 🔄

Recurrent Neural Networks

RNN, LSTM, GRU for sequence modeling

 🔧

TensorFlow & Keras

Build deep learning models with TensorFlow 2.x

Part 5: Model Evaluation & Optimization

✅

Model Evaluation Metrics

Accuracy, precision, recall, F1-score, ROC-AUC

 🔀

Cross-Validation

K-fold, stratified, time series validation

 ⚙️

Hyperparameter Tuning

Grid search, random search, Bayesian optimization

 📊

Overfitting & Regularization

L1/L2 regularization, dropout, early stopping

Part 6: Feature Engineering & Selection

🔨

Feature Engineering

Creating powerful features from raw data

 🎯

Feature Selection

Filter, wrapper, embedded methods

 ⚖️

Handling Imbalanced Data

SMOTE, class weights, sampling techniques

Part 7: Advanced Topics

🎭

Ensemble Methods

Bagging, boosting, stacking, XGBoost, LightGBM

 📅

Time Series Analysis

ARIMA, Prophet, LSTM for forecasting

 💬

NLP with ML

Text preprocessing, TF-IDF, word embeddings

 👁️

Computer Vision

Image processing, object detection, segmentation

 🎬

Recommender Systems

Collaborative filtering, content-based, hybrid

 🎮

Reinforcement Learning

Q-learning, policy gradients, deep RL

Part 8: MLOps & Production

🚀

Model Deployment

Flask APIs, Docker, cloud deployment

 ⚙️

MLOps Best Practices

CI/CD, model versioning, monitoring

 🔍

Model Interpretability

SHAP, LIME, feature importance

 🤖

AutoML

Automated ML pipelines, H2O, AutoKeras

🛠️ Hands-On Projects

Build 12 production-ready machine learning projects:

🏠 Beginner

House Price Prediction

Build a regression model to predict house prices using multiple features

Scikit-learn Pandas Regression
📧 Beginner

Spam Email Classifier

Text classification using Naive Bayes and TF-IDF

NLP Classification Naive Bayes
👥 Beginner

Customer Segmentation

Cluster customers using K-Means for targeted marketing

K-Means Clustering EDA
💳 Intermediate

Credit Card Fraud Detection

Handle imbalanced data for fraud detection with ensemble methods

XGBoost SMOTE Imbalanced Data
🖼️ Intermediate

Image Classifier with CNN

Build a deep CNN for multi-class image classification

TensorFlow CNN Transfer Learning
📈 Intermediate

Stock Price Prediction

Time series forecasting with LSTM neural networks

LSTM Time Series Keras
😊 Intermediate

Twitter Sentiment Analysis

Analyze sentiment in tweets using deep learning

RNN NLP Word2Vec
🎬 Intermediate

Movie Recommendation System

Build a hybrid recommender with collaborative filtering

Matrix Factorization Surprise Hybrid
🚗 Advanced

Object Detection (YOLO)

Real-time object detection using YOLO architecture

YOLO Computer Vision PyTorch
💬 Advanced

ML-Powered Chatbot

Build an intelligent chatbot with seq2seq models

Seq2Seq Attention TensorFlow
👤 Advanced

Face Recognition System

Build a face recognition system with deep learning

FaceNet Siamese Networks OpenCV
🔄 Advanced

End-to-End ML Pipeline

Production ML system with deployment and monitoring

MLflow Docker FastAPI

🛠️ Tools & Technologies

📊 Core Libraries

  • NumPy & Pandas
  • Scikit-learn
  • Matplotlib & Seaborn
  • SciPy

🧠 Deep Learning

  • TensorFlow 2.x
  • Keras
  • PyTorch
  • JAX

⚡ Advanced ML

  • XGBoost & LightGBM
  • CatBoost
  • H2O AutoML
  • Optuna

🚀 MLOps

  • MLflow
  • Docker & Kubernetes
  • FastAPI & Flask
  • AWS SageMaker

📋 Prerequisites

🐍

Python Programming

Solid understanding of Python syntax, functions, OOP

📐

Mathematics

Basic linear algebra, calculus, probability & statistics

📊

Data Analysis

Familiarity with data manipulation and visualization

💻

Development Tools

Jupyter notebooks, Git, command line basics

🚀 Ready to Master Machine Learning?

Start with fundamentals or jump to advanced topics based on your level

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