Machine Learning Curriculum
Module 1: Introduction to Machine Learning
- What is Machine Learning?
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Types of Machine Learning: Supervised, Unsupervised,
Reinforcement Learning
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Applications of Machine Learning in real-world scenarios
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Basic concepts: Features, Labels, Training, Testing,
Validation
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Tools and libraries: Introduction to Python, Anaconda, Jupyter
Notebooks
Module 2: Data Preprocessing and Exploration
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Data collection and acquisition using Kaggle and other
repositories
- Data cleaning and handling missing values
- Data transformation and normalization
- Exploratory Data Analysis (EDA)
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Visualization techniques using libraries like Matplotlib and
Seaborn
Module 3: Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines
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Model evaluation and metrics: Accuracy, Precision, Recall,
F1-score, ROC curve
Module 4: Unsupervised Learning Algorithms
- Clustering: K-Means, Hierarchical Clustering
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Dimensionality Reduction: PCA (Principal Component Analysis)
- Anomaly detection
- Applications of unsupervised learning
Module 5: Model Evaluation and Hyperparameter Tuning
- Cross-validation techniques
- Bias-Variance trade-off
- Grid Search and Random Search for hyperparameter tuning
- Overfitting and underfitting mitigation
Module 6: Neural Networks and Deep Learning
- Introduction to neural networks
- Activation functions and architecture
- Feedforward Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transfer learning and pre-trained models
Module 7: Natural Language Processing (NLP)
- Text preprocessing
- Bag of Words
- Sentiment analysis
- Word embeddings: Word2Vec, GloVe
- Introduction to Recurrent Neural Networks for NLP
Module 8: Time Series Analysis
- Introduction to time series data
- Moving averages and exponential smoothing
- ARIMA (AutoRegressive Integrated Moving Average) models
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LSTM (Long Short-Term Memory) for time series forecasting
Module 9: Capstone Project
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Participants work on a real-world Machine Learning project
- Applying concepts from previous modules
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Data exploration, preprocessing, model selection, evaluation,
and interpretation
- Project presentation and peer review