Machine Learning

1. Introduction to Machine Learning

• Definition, and Applications of machine learning
• Types of machine learning: supervised, unsupervised, reinforcement learning
• Machine learning workflow: data collection, preprocessing, model training, evaluation, and deployment
• Ethical Considerations and Challenges in machine learning

2. Exploratory Data Analysis (EDA)

• Data preprocessing: handling missing values, data cleaning, feature scaling
• Data visualization techniques: histograms, scatter plots, box plots, correlation matrices
• Statistical analysis of data: descriptive statistics, central tendency, variability
• Feature engineering: selecting relevant features, creating new features, handling categorical variables

3. Supervised Learning Algorithms

• Linear regression: simple linear regression, multiple linear regression, regularization
• Logistic regression: binary classification, multi-class classification, model evaluation
• Support Vector Machines (SVM): linear SVM, kernel SVM, hyperparameter tuning
• Decision trees: construction, pruning, ensemble methods (random forests)

4. Unsupervised Learning Algorithms

• Clustering algorithms: K-means, hierarchical clustering, DBSCAN
• Dimensionality reduction techniques: Principal Component Analysis (PCA), t-SNE
• Association rule learning: Apriori algorithm, frequent itemsets, association rules
• Anomaly detection: outlier detection, isolation forest.

5. Evaluation Metrics and Model Selection

• Performance evaluation metrics: accuracy, precision, recall, F1-score, ROC curve, AUC-ROC
• Cross-validation techniques: k-fold cross-validation, stratified cross-validation
• Hyperparameter tuning: grid search, random search, model selection techniques
• Model evaluation strategies: overfitting, underfitting, bias-variance tradeoff.

6. Neural Networks and Deep Learning

• Basics of artificial neural networks: neurons, activation functions, feedforward networks
• Deep learning architectures: 
• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)
• Backpropagation algorithm and gradient descent optimization
• Transfer learning and pre-trained models

7. Natural Language Processing (NLP)

• Text preprocessing techniques: tokenization, stemming, stop-word removal
• Text representation: bag-of-words, TF-IDF, word embeddings (Word2Vec, GloVe)
• NLP tasks: sentiment analysis, text classification, named entity recognition, machine translation
• Language models: recurrent neural networks (RNNs), long short-term memory (LSTM)
• Recommendation Systems

8. Collaborative filtering: user-based and item-based filtering

• Content-based filtering: feature extraction, similarity measures
• Hybrid recommendation systems combining collaborative and content-based filtering
• Evaluation of recommendation systems: precision, recall, mean average precision

9. Time Series Analysis

1. Time series data characteristics: trend, seasonality, autocorrelation
2. Time series forecasting methods: moving average, exponential smoothing, ARIMA
3. Long Short-Term Memory (LSTM) networks for time series prediction.
4. Evaluating forecast accuracy: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)

10. Reinforcement Learning

• Markov Decision Processes (MDPs) and the agent-environment interaction
• Policy-based, value-based, and model-based reinforcement learning algorithms
• Q-learning and Deep Q-Networks (DQN) for learning optimal policies
• Applications of reinforcement learning: game playing, robotics, control systems