Beginner Level
1. Introduction to Machine Learning
- What is Machine Learning?
- Definition and importance in data science
- Types of machine learning: supervised, unsupervised, and reinforcement learning
2. Basic Concepts
- Key Terminologies
- Features, labels, training set, test set
- Overfitting and underfitting
- Machine Learning Process
- Problem definition, data collection, data preparation, model training, evaluation, and deployment
3. Data Preprocessing
- Understanding Data Preparation
- Data cleaning (handling missing values, duplicates)
- Feature scaling (normalization and standardization)
- Encoding categorical variables (one-hot encoding, label encoding)
4. Supervised Learning
- Regression
- Linear regression: simple and multiple
- Evaluation metrics: mean absolute error (MAE), mean squared error (MSE), R-squared
- Classification
- Logistic regression
- Evaluation metrics: accuracy, precision, recall, F1-score
5. Introduction to Libraries and Tools
- Common Libraries
- Introduction to Scikit-learn, Pandas, NumPy, and Matplotlib
- Basic data manipulation and visualization
Intermediate Level
1. Advanced Supervised Learning
- Tree-Based Models
- Decision trees: understanding tree structure, splitting criteria
- Random forests: bagging, feature importance
- Support Vector Machines (SVM)
- Understanding hyperplanes and kernel trick
- SVM for classification and regression
2. Unsupervised Learning
- Clustering
- K-means clustering: algorithm, evaluation, and applications
- Hierarchical clustering: agglomerative vs. divisive
- Dimensionality Reduction
- Principal Component Analysis (PCA): theory and implementation
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
3. Model Evaluation and Validation
- Cross-Validation Techniques
- k-fold cross-validation, stratified sampling
- Hyperparameter Tuning
- Grid search and random search for model optimization
4. Feature Engineering
- Understanding Feature Importance
- Creating new features from existing data
- Techniques for feature selection (filter, wrapper, embedded methods)
5. Introduction to Neural Networks
- Basics of Neural Networks
- Understanding perceptrons and multi-layer perceptrons (MLPs)
- Activation functions and their roles
Advanced Level
1. Deep Learning
- Introduction to Deep Learning
- Understanding deep neural networks (DNNs)
- Key architectures: convolutional neural networks (CNNs), recurrent neural networks (RNNs)
- Frameworks and Libraries
- Overview of TensorFlow and Keras for building deep learning models
2. Natural Language Processing (NLP)
- Text Data Processing
- Tokenization, stemming, lemmatization
- Bag-of-words and TF-IDF representation
- NLP Models
- Sentiment analysis, text classification, and topic modeling
3. Time Series Analysis and Forecasting
- Time Series Forecasting Techniques
- Understanding seasonal decomposition, ARIMA, and exponential smoothing
- Machine Learning for Time Series
- Feature engineering for time series data and applying regression techniques
4. Ensemble Learning
- Combining Models
- Bagging and boosting techniques (e.g., Random Forests, Gradient Boosting, AdaBoost)
- Understanding stacking and blending methods
5. Deployment and Productionization
- Model Deployment Strategies
- Introduction to model serving (using Flask, FastAPI, or cloud services)
- Monitoring and maintaining models in production
