Machine Learning Full Course | Beginner to Advanced (FREE) 2026

Wed, 11 Mar 2026 11:46:12 +0000

In this video, you will learn Machine Learning from Beginner to Advanced level step by step.

⬇️ Download the Full Machine Learning Notes here: https://www.theiscale.com/Data....Analytics/machine-le

This complete Machine Learning course is designed for:
✔ Beginners with no prior experience
✔ College students & freshers
✔ Data Science & AI aspirants
✔ Working professionals

📌 What you’ll learn in this course:
• Introduction to Machine Learning with AI
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Linear & Logistic Regression
• Decision Trees & Random Forest
• KNN, Naive Bayes, SVM
• Clustering (K-Means, Hierarchical)
• Feature Engineering & Data Preprocessing
• Model Evaluation & Optimization
• Real-world Machine Learning Projects
• Career roadmap for ML & Data Science

Timestamp
00:00:00 -Course Introduction
00:03:24 -Technological Evolution: Web to Apps to ML
00:06:50 -Mapping the AI Branch: ML vs. Deep Learning vs. Data Science
00:13:33 -Dynamic Pricing Models and Negotiation Algorithms
00:15:45 -Chatbots and Virtual Assistants
00:16:01 -Sentiment Analysis, Customer Reviews
00:17:21 -The Machine Learning Model Workflow
00:17:44 -Data Pre-processing and Missing Values
00:18:20 -Model Training and Iterative Evaluation
00:19:19 -The Pillars of ML: Math, Coding, and Implementation
00:20:53 -Supervised, Unsupervised, and Reinforcement
00:21:37 - Reinforcement Learning: Agents, Environments, and Rewards
00:23:02 -Supervised Learning
00:25:37 -Regression Problems
00:27:27 -Classification Problems
00:28:10 - Loan Approval and Email Spam Categorization
00:28:47 - Dependent vs. Independent Features
00:32:00 - Binary vs. Multi-class Classification
00:34:18 - Unsupervised Learning
00:35:54 - Clustering and Pattern Recognition
00:39:57 - Dimensionality Reduction Explained
00:42:01 -Linear Regression Algorithm
00:43:00 - Introduction to Linear Regression
00:45:08 - The Concept of the Best Fit Line
00:48:10 - ML Notation
00:49:07 - Intercepts and Slopes Defined
00:51:59 - Defining the Cost Function
00:53:53 - Error Minimization Strategy
00:57:48 - Gradient Descent Logic and Global Minima
01:04:23 - Practical: Setting up Anaconda and Jupyter Notebook
01:06:55 - Practical: Housing Price Prediction Coding
01:08:14 - Importing NumPy, Pandas, and Matplotlib
01:14:09 - Dividing Data into X and Y Features
01:19:50 - Implementing Cross-Validation Scores
01:34:16 - Interpreting Final Accuracy Results
01:34:17 - Ridge & Lasso Algorithm
01:35:42 - Overfitting and Underfitting
01:37:04 - Bias vs.Variance Trade-off
01:39:56 -Ridge Regression
01:41:05 -Handling Multi-Collinearity
01:43:42 -Lasso Regression
01:46:30 -Feature Selection via Lasso
01:52:12 -Grid Search CV Implementation
01:58:12 -Standard Scaling of Features
01:59:10: Logistic Regression Algorithm
02:04:43 -Introduction to Logistic Regression
02:07:06 -Sigmoid Function Mathematics
02:07:54 -Linear Regression Line
02:08:13 -Decision Boundaries
02:09:57 -Logistic Regression Cost Function Issues
02:14:42 -The Confusion Matrix
02:16:40 -True Positives, False Positives, and Errors
02:18:32 -Precision, Recall, F1-Score
02:22:26 -Practical: Breast Cancer Prediction Implementation
02:41:21 -Visualizing the Confusion Matrix with Heatmaps
02:45:47-Naive Bayes Algorithm Fundamentals
02:46:18 -Dependent and Independent Events
02:50:50 -Bayes Theorem Formula Construction
02:54:01 -Gaussian Naive Bayes Implementation
03:08:47 -K-Nearest Neighbors (KNN) Theory
03:12:32 -Manhattan vs. Euclidean Distance
03:27:48 -Decision Tree Algorithm Structure
03:30:46 -Root Nodes, Leaf Nodes, and Pure Splits
03:35:16 -Entropy and Gini Impurity Calculations
03:44:01 -Information Gain and Feature Selection
03:55:05 -Practical: with Scikit-Learn
03:56:02 - Unsupervised Algorithms
04:13:45 -Ensemble Techniques: Bagging vs. Boosting
04:15:56 -Bootstrap Aggregating
04:18:38 -Random Forest Algorithm
04:33:45 -AdaBoost Algorithm
04:57:52 -K-Means Clustering Algorithm
05:00:30 -The Elbow Method for Optimal Clusters
05:11:14 -Hierarchical Clustering and Dendrograms
05:16:50 -DBScan: Density-Based Clustering
05:24:44 -Final Summary and Career Roadmap

⚙️ Tools & Libraries:
Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
👉 By the end of this course, you will be confident to build Machine Learning models from scratch.

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