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Machine Learning

Machine Learning (ML) is at the forefront of technological innovation, driving advancements in various industries such as healthcare, finance, transportation, and entertainment. This course offers a comprehensive introduction to the principles, algorithms, and applications of machine learning, equipping students with the knowledge and skills needed to build and deploy machine learning models for real-world problems. From understanding the theoretical foundations of ML to implementing algorithms and evaluating model performance, this course covers essential topics for anyone interested in mastering the art of machine learning.

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Course Topics

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  1. Introduction to Machine Learning

    • Overview of machine learning concepts, terminology, and applications

    • Types of machine learning (supervised, unsupervised, reinforcement learning)

    • Ethical considerations and bias in machine learning

  2. Data Preprocessing and Exploration

    • Data cleaning, transformation, and feature engineering

    • Exploratory data analysis (EDA) techniques

    • Handling missing values, outliers, and imbalanced datasets

  3. Supervised Learning Algorithms

    • Linear regression and logistic regression

    • Decision trees and ensemble methods (random forests, gradient boosting)

    • Support vector machines (SVM) and k-nearest neighbors (KNN)

  4. Unsupervised Learning Algorithms

    • K-means clustering and hierarchical clustering

    • Principal component analysis (PCA) and dimensionality reduction

    • Association rule mining and anomaly detection

  5. Introduction to Deep Learning

    • Basics of neural networks and deep learning architectures

    • Convolutional neural networks (CNNs) for image classification

    • Recurrent neural networks (RNNs) for sequence modeling

  6. Model Evaluation and Validation

    • Performance metrics for classification and regression tasks

    • Cross-validation techniques and hyperparameter tuning

    • Bias-variance tradeoff and overfitting/underfitting

  7. Advanced Topics in Machine Learning

    • Reinforcement learning and Markov decision processes

    • Natural language processing (NLP) and text mining

    • Transfer learning and model deployment considerations

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