Course:ML (Machine Learning) - Online

→ Prerequisite: Proficiency in programming, mathematics (linear algebra, calculus, and statistics), and a strong foundation in Python programming.

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→ Rationale: Machine Learning is a vital field within Artificial Intelligence that focuses on the development of algorithms and models that enable computers to learn and make predictions from data. This course will provide you with a comprehensive understanding of machine learning techniques and their practical applications.

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	content
→	Unit 1: Introduction to Machine Learning 1.1 - Overview of Machine Learning. 1.2 - Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning). 1.3 - The Machine Learning workflow.
→	Unit 2: Data Preprocessing and Feature Engineering 2.1 - Data cleaning and transformation. 2.2 - Feature selection and extraction. 2.3 - Handling missing data and outliers.
→	Unit 3: Supervised Learning 3.1 - Linear and logistic regression. 3.2 - Decision trees and ensemble methods. 3.3 - Support vector machines and k-Nearest Neighbors.
→	Unit 4: Unsupervised Learning 4.1 - Clustering algorithms (K-Means, Hierarchical, DBSCAN). 4.2 - Dimensionality reduction techniques (PCA). 4.3 - Anomaly detection and association rule mining.
→	Unit 5: Deep Learning and Neural Networks 5.1 - Introduction to artificial neural networks. 5.2 - Training deep learning models. 5.3 - Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
→	Unit 6: Model Evaluation and Hyperparameter Tuning 6.1 - Cross-validation and model evaluation metrics. 6.2 - Overfitting and underfitting. 6.3 - Hyperparameter optimization.
→	Unit 7: Machine Learning Projects 7.1 - Real-world machine learning applications. 7.2 - Designing and implementing machine learning solutions. 7.3 - Presenting and documenting results.
→	Unit 8: Ethics and Future Trends 8.1 - Ethical considerations in machine learning. 8.2 - Emerging trends and challenges. 8.3 - Preparing for a career in machine learning.