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Free Download ML & AI Foundations From Intuition to Implementation
Published 1/2026
Created by Swapnil Daga
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 62 Lectures ( 4h 7m ) | Size: 5.3 GB
Learn fundamentals of ML & AI in a practical manner by building hands-on projects that can be added in your resume.
What you'll learn
✓ Understand the basic maths & programming used to build projects in AI & ML
✓ Get practical idea of basic and advanced ML Concepts
✓ Learn to build hands-on AI & ML Projects from Scratch
✓ Complete your interview preparation for AI Based Roles by showcasing the projects effectively in your resume & being prepared for FAQ's on the built projects
✓ Confidently explain ML Concepts in Interviews
✓ Build & Debug Models on your own
✓ Think beyond black-box ML
✓ Choose the right model for the right problem
Requirements
● Basic Knowledge of Python or willingness to learn basic python on the go.
● Basic high school maths like matrix multiplication and vector operations.
Description
This course builds strong ML foundations by combining clear intuition, solid math, and hands-on implementation.
You won't just use ML libraries - you'll understand how models work internally, why they work, and when they fail.
After completing this course, you will
• Think beyond black-box ML
• Confidently explain ML concepts in interviews
• Build and debug models on your own
• Choose the right model for the right problem
In short: from following tutorials → to real ML understanding.
This course is ideal for
• Students & freshers aiming for ML/Data roles
• Software professionals transitioning into ML
• Anyone who knows "some ML" but lacks confidence
This course helps you upgrade your career by building real ML depth, not just surface knowledge.
What is covered?
• Math foundations for ML (basic → advanced)
• Core models: Linear & Logistic Regression, Decision Trees, Neural Networks
• Ensemble methods: Bagging, Boosting, Random Forest
• Optimizers, regularization, overfitting & bias-variance tradeoff
• Hands-On Learning
• Movie rating classification (Kaggle + GPUs)
• Neural Network implementation from scratch
• Music genre classification using MFCC + Neural Networks
• Interview preparation session for all covered topics
In one line
A practical, concept-driven ML course that turns learners into confident ML engineers
Detailed Course Breakdown
• Section 1 : Overview
- Introduction to the Instructor & Course
- Why knowledge of basic maths is crucial for intuition in AI & ML
- Things we will be learning during the course
• Section 2: Probability & Statistics
- Probability & Stats
- Mean, Median & Mode
- Calculation Expected Value
- Variance & Covariance
- Normal Distribution
- Central Limit Theorem
- Conditional Probability
- Baye's Theorem
- Maximum Likelihood Estimation
• Section 3: Linear Algebra
- Overview of Linear Algebra
- Scalar, Vectors, Matrix & Tensors
- Matrix Operations
- Rank & Linear Dependence
- Eigen Vectors & Eigen Values
- Principle Component Analysis
• Section 4: Calculus
- Overview of Calculus
- Derivatives & Gradients
- Gradient Descent Algorithm
- Chain Rule
- Fundamentals of Optimisation
- Local vs Global Maxima
- Convexity
• Section 5: Basics of Python
- Practical Python for ML & AI
• Section 6: Introduction to ML
- Overview & Introduction to ML
- Basics of ML
- Classification of ML
- Regression vs Classification
- Trainset / Validation Set / Testset
- Overfitting (Learning vs Memories)
• Section 7: Training of Models
- One-Hot Encoding
• Section 8: Regression Methods
- Linear Regression
- Parameters to tests models
• Section 9: Decision Trees
- Introduction to Decision Trees
- Training & Testing Process
- I.G in Decision Trees
- G.I in Decision Trees
• Section 10: Ensembles
- Introduction to Ensembles
- Bagging
- Boosting
• Section 11: Training of Models
- Practical Training Methodology
• Section 12: Advanced Machine Learning
- Overview in Advanced Machine Learning
• Section 13: Logistic Regression
- What is Logisitic Regression ?
- Why Logistic Regression ?
- Maths behind Logisitic Regression?
- Do I always need Binary Classification?
• Section 14: Neural Networks
- Architecture & Overview
- Dive into Neural Network
- Generalization
- Batch Processing
- Optimizer
• Section 15: Demo
- Kaggle Tutorial
- Demo for Projects & Model Training
• Section 16: Hands-On Practical Implementation of Projects
- Hands-on Logistic Regression Coding
- Hands-on Decision Trees Coding
- Hands-on Neural Network Coding
- Neural Network Coding for Multi Category Classification
• Section 17: Interview Preparation for Prepared Projects
- FAQ in Interviews on projects discussed in the course
Who this course is for
■ Students & freshers aiming for ML/Data roles
■ Software professionals transitioning into ML
■ Anyone who knows "some ML" but lacks confidence
Homepage
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