Machine Learning Tutorial - Practical Implementation and Explanation of ML Algorithms

Learn fundamental machine learning algorithms through practical implementation and step-by-step explanations in this comprehensive 4-hour tutorial. Explore the foundations of artificial intelligence and machine learning before diving into supervised learning algorithms including logistic regression, Naive Bayes with Bayesian theorem principles, decision trees with entropy and information gain concepts, k-nearest neighbors (KNN), random forest and ensemble methods, and support vector machines (SVM). Master regression techniques through linear and polynomial regression implementations using Python. Discover unsupervised learning with k-means clustering algorithms and dimensionality reduction using Principal Component Analysis (PCA). Address critical machine learning concepts like overfitting and underfitting with practical examples. Gain hands-on experience with Python implementations for text classification using multinomial Naive Bayes, and work through practical examples for each algorithm to solidify your understanding of machine learning fundamentals.

Syllabus

What is Artificial Intelligence?
What is Machine Learning?
Logistic Regression Explained with Practical example
How Bayesian Theorem Works? (Naive Bayes Algorithm)
Naive Bayes Algorithm Using Python
Multinomial Naive Bayes Using Python | Text Classification Using Naive Bayes |
Decision Tree | Information Gain | Entropy | Piford Technologies | Math for Decision Tree
K Nearest Neighbour | KNN Algorithm | KNN in Python
Random Forest | Bagging | Ensemble Method
Support Vector Machine | SVM Explained
Practical Example on Support Vector Machine | SVM Explained
Linear Regression Algorithm | Linear Regression Using Python
Polynomial Regression Algorithm | Polynomial Regression Using Python
K-Means Clustering Algorithm (Unsupervised Learning)
Overfitting and Underfitting explained with Examples
Basics Of Principal Component Analysis (PCA)