Skill Flow

S.No	Skill	Semester
1	Foundations of ML: Python and Mathematical Essentials Introduction to Machine Learning, Python Basics for ML - Python Data Types and Structure - Control Flow and Functions in Python - NumPy and Pandas for Data Manipulation. Linear Algebra for ML, Calculus in ML, Probability and Statistics, Optimization in ML, Capstone Project - Applying Mathematical Foundations to a Real-world ML Problem	01
2	Linear Regression and Beyond: A Deep Dive into ML Models Introduction to Linear Regression, Linear Regression Variants, Introduction to Non-linear Regression Models. Logistic Regression, Support Vector Machines (SVM), Decision Trees and Random Forests, Gradient Boosting Algorithms, Practical Applications and Case Studies, Hands-On Projects.	02
3	From Data to Decisions: A Classification Journey in Machine Learning Introduction to Classification, Foundations of Data Preparation, Feature Engineering for Classification, Supervised Learning Basics, Naive Bayes Classification, Deep Learning for Classification, Evaluation Metrics for Classification, Handling Imbalanced Data, Hyperparameter Tuning, Interpretability in Classification Models, Practical Applications and Case Studies, Hands-On Projects	03
4	The Edge of Prediction: Probability-Based ML Models AutodIntroduction to Probability in Machine Learning, Bayesian Thinking in ML, Probabilistic Classification Models, Probabilistic Regression Models, Uncertainty in Neural Networks, Handling Imbalanced Data with Probabilities, Practical Applications of Probability Models, Challenges and Future Trends in Probabilistic ML, Hands-On Projects.esk	04
5	Clustering Chronicles: Navigating ML's Hidden Structures Introduction to Clustering, Foundations of Data Preprocessing for Clustering, K-Means Clustering, Hierarchical Clustering, Density-Based Clustering, Gaussian Mixture Models (GMM), Dimensionality Reduction for Clustering, Cluster Interpretability, Handling Large Datasets with Clustering, Applications of Clustering in Machine Learning, Emerging Trends in Clustering Research, Hands-On Projects.	05

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