**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**

- Time series data characteristics: trend, seasonality, autocorrelation
- Time series forecasting methods: moving average, exponential smoothing, ARIMA
- Long Short-Term Memory (LSTM) networks for time series prediction.
- 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

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