Machine Learning Core Blueprint (Algorithms + Intuition)

About Course

This module is designed to provide a deep dive into the foundational algorithms of Machine Learning, focusing on both the mathematical intuition and the practical implementation of core ML algorithms. Whether you’re a beginner or someone looking to strengthen your machine learning knowledge, this module will guide you through the essential concepts that drive most modern AI applications. By the end of this course, you will have a clear understanding of how algorithms work, when to use them, and how to implement them from scratch.

You’ll gain insights into supervised, unsupervised, and reinforcement learning techniques, covering popular algorithms such as linear regression, decision trees, k-nearest neighbors (KNN), support vector machines (SVM), and k-means clustering, among others.

Mathematical Intuition Behind Machine Learning Algorithms: Gain an understanding of the math that powers ML algorithms, including gradient descent, cost functions, and optimization.
Supervised Learning Algorithms: Learn how linear regression, logistic regression, decision trees, and support vector machines (SVM) work, and how to apply them.
Unsupervised Learning Algorithms: Understand techniques like k-means clustering, principal component analysis (PCA), and hierarchical clustering.
Evaluation Metrics: Learn how to evaluate models using metrics like accuracy, precision, recall, F1 score, and ROC curves.
Model Tuning: Understand hyperparameter tuning, cross-validation, and model optimization to improve the performance of machine learning models.
Ensemble Methods: Learn about advanced techniques like random forests and gradient boosting.

Course Content

Introduction to Machine Learning

Overview of Machine Learning and its Applications
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Key Terminologies: Model, Features, Target, Training, and Testing Data

Supervised Learning: Regression

Supervised Learning: Classification

Unsupervised Learning: Clustering

Ensemble Learning

Reinforcement Learning (Intro)

Model Optimization & Hyperparameter Tuning

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Machine Learning Core Blueprint (Algorithms + Intuition)

About Course

What Will You Learn?

Course Content

Introduction to Machine Learning

Overview of Machine Learning and its Applications

Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

Key Terminologies: Model, Features, Target, Training, and Testing Data

Supervised Learning: Regression

Linear Regression: Understanding the math behind linear regression, cost functions, and gradient descent.

Logistic Regression: Classification problems, binary classification, and model evaluation.

Model Evaluation: Accuracy, precision, recall, F1 score, and ROC/AUC.

Supervised Learning: Classification

Decision Trees: How decision trees work, advantages, and overfitting.

Support Vector Machines (SVM): Hyperplanes, kernels, and margin maximization for classification.

K-Nearest Neighbors (KNN): Working with non-linear data and measuring distances in high-dimensional spaces.

Unsupervised Learning: Clustering

K-Means Clustering: How k-means works, choosing k, and practical applications.

Hierarchical Clustering: Agglomerative vs. divisive clustering and applications.

Principal Component Analysis (PCA): Reducing dimensionality and visualizing high-dimensional data.

Ensemble Learning

Random Forests: Using multiple decision trees for more accurate predictions.

Boosting and Gradient Boosting: Techniques for improving model accuracy by combining weak learners.

Reinforcement Learning (Intro)

Basics of reinforcement learning and how machines learn through rewards and punishments.

Applications of reinforcement learning in real-world problems.

Model Optimization & Hyperparameter Tuning

Techniques like grid search, random search, and cross-validation to tune and improve machine learning models.

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