Welcome to the Machine Learning Cohort! This README provides an overview of the topics, subtopics, and mini projects covered throughout the duration of the program.
- Topics: Definition of ML, Types of ML algorithms
- Subtopics: Supervised, unsupervised, and reinforcement learning; Basics of data preprocessing; Exploratory Data Analysis (EDA)
- Mini Project: Predicting House Prices
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Topics: Introduction to regression
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Subtopics: Linear regression theory and implementation, Polynomial regression
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Mini Project: Predicting Student Exam Scores
- Topics: Introduction to classification
- Subtopics: Logistic regression, Decision Trees and Random Forests
- Mini Project: Predicting Student's Placement
- Mini Project: Email Spam Classification
- Topics: Introduction to clustering
- Subtopics: K-Means clustering, Hierarchical clustering
- Mini Project: Customer Segmentation
- Topics: Introduction to dimensionality reduction
- Subtopics: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Mini Project: Visualization of High-Dimensional Data
- Topics: Cross-validation techniques, Grid search and Random search for hyperparameter tuning
- Subtopics: Model selection criteria, Introduction to ensemble methods (Bagging, Boosting)
- Mini Project: Model Selection for Iris Classification
- Topics: Introduction to artificial neural networks (ANNs)
- Subtopics: Basics of feedforward neural networks, Activation functions, Backpropagation algorithm
- Mini Project: Handwritten Digit Recognition
- Topics: Introduction to deep learning
- Subtopics: Convolutional Neural Networks (CNNs) for image classification, Recurrent Neural Networks (RNNs) for sequence data, Long Short-Term Memory (LSTM) networks
- Mini Project: Image Classification with CNNs
- Topics: Introduction to NLP
- Subtopics: Basics of text preprocessing, Bag of Words (BoW) model, Introduction to Word Embeddings (Word2Vec, GloVe)
- Mini Project: Sentiment Analysis
- Topics: Formulation of a machine learning project idea (can be based on any real-world problem)
- Subtopics: Project implementation, Presentation and discussion of project results
- Assignments and Exercises: Regular assignments to reinforce learning
- Q&A Sessions: Opportunities for students to ask questions and clarify doubts
- Project Guidance: Continuous guidance and support for the final project
- Feedback: Regular feedback sessions to assess progress and provide improvement suggestions
- Each day's topic and subtopic are organized into folders.
- Inside each folder, you'll find relevant resources, code snippets, and instructions for mini projects.
- Feel free to explore, experiment, and collaborate with your cohort members.
Happy learning!
This README is subject to updates and modifications as the program progresses.