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Curriculum
- 1 Section
- 75 Lessons
- 30 Hours
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- Machine Learning A-Z: Hands-On with Python & Java75
- 1.1Introduction to Machine Learning
- 1.2What is Machine Learning?
- 1.3Types: Supervised, Unsupervised, Reinforcement Learning
- 1.4ML in real-world industries
- 1.5Python vs Java in ML
- 1.6Set up Python (Anaconda, Jupyter)
- 1.7Set up Java (JDK, IntelliJ or Eclipse, Weka or DL4J)
- 1.8Data Preprocessing & Exploration
- 1.9Loading datasets (CSV, JSON, Excel)
- 1.10Handling missing data
- 1.11Data encoding (Label, One-Hot)
- 1.12Normalization & scaling
- 1.13Train-test split
- 1.14Python: Pandas, NumPy
- 1.15Java: Apache Commons + Weka
- 1.16Regression Models
- 1.17Linear Regression
- 1.18Polynomial Regression
- 1.19Evaluation metrics: MSE, RMSE, R²
- 1.20Python: Predict house prices using scikit-learn
- 1.21Java: Implement regression using Weka or DL4J
- 1.22Classification Algorithms
- 1.23Logistic Regression
- 1.24k-Nearest Neighbors
- 1.25Decision Trees
- 1.26Random Forest
- 1.27Naive Bayes
- 1.28Python: Spam detector using scikit-learn
- 1.29Java: Email classification using Weka
- 1.30Unsupervised Learning
- 1.31K-Means Clustering
- 1.32Hierarchical Clustering
- 1.33Dimensionality Reduction (PCA)
- 1.34Python: Customer segmentation using K-Means
- 1.35Java: Clustering iris dataset with Weka
- 1.36Model Evaluation & Optimization
- 1.37Cross-validation
- 1.38Confusion matrix, Precision, Recall, F1-Score
- 1.39Grid search & hyperparameter tuning
- 1.40Bias-variance tradeoff
- 1.41Use GridSearchCV in Python
- 1.42Implement evaluation in Java using Weka’s Eval class
- 1.43Deep Learning Essentials
- 1.44Intro to Neural Networks
- 1.45Forward/backpropagation
- 1.46Activation functions
- 1.47Feedforward vs CNN/RNN (basic concepts)
- 1.48Python: MNIST digit recognizer using TensorFlow/Keras
- 1.49Java: Neural Net with DL4J (DeepLearning4J)
- 1.50Natural Language Processing
- 1.51Text cleaning & preprocessing
- 1.52Bag of Words, TF-IDF
- 1.53Sentiment Analysis
- 1.54Python: Movie review sentiment classifier
- 1.55Java: Text analysis with OpenNLP or DL4J NLP module
- 1.56Real-Time Deployment & Integration
- 1.57Saving/loading models
- 1.58REST API with Flask (Python)
- 1.59REST API with Spring Boot (Java)
- 1.60Calling models in production apps
- 1.61Deploy ML model as API (Python & Java versions)
- 1.62Frontend demo: Prediction via REST endpoint
- 1.63Capstone Project & Certification
- 1.64Fraud Detection (Classification)
- 1.65Stock Price Prediction (Regression + LSTM – optional stretch)
- 1.66Chatbot Sentiment Engine (NLP)
- 1.67Customer Segmentation (Clustering)
- 1.68Python + Java source code
- 1.69GitHub repository
- 1.70Final project report & video walkthrough
- 1.71🔍 Quizzes & weekly mini-challenges
- 1.72📦 Dataset packs for all modules
- 1.73🛠️ Code templates in both Python & Java
- 1.74📜 Certificate of Completion
- 1.75📘 PDF Handbook: “ML Cheatsheets for Python & Java”
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