ML & AI Foundations From Intuition to Implementation - Printable Version +- Softwarez.Info - Software's World! (https://softwarez.info) +-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone) +--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials) +--- Thread: ML & AI Foundations From Intuition to Implementation (/Thread-ML-AI-Foundations-From-Intuition-to-Implementation)

ML & AI Foundations From Intuition to Implementation - OneDDL - 01-28-2026 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 [To see links please register or login] Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [To see links please register or login] No Password - Links are Interchangeable