FREE 🔥 Machine Learning Course Part 4 😱 | Real Projects + 40 Datasets | Practical Training | 🔥 2026 скачать в хорошем качестве

FREE 🔥 Machine Learning Course Part 4 😱 | Real Projects + 40 Datasets | Practical Training | 🔥 2026

Machine Learning is NOT just theory — this is where REAL practical learning begins 🔥 Welcome to the FREE Full Machine Learning Course – Part 4 (2026) 🚀 In this video, you’ll learn Machine Learning with real-world datasets, hands-on training, and industry-level projects — taught step by step in a practical way. 👉 If you want to become a Machine Learning Engineer, Data Scientist, or AI Professional, this video is a MUST-WATCH. ✅ What You’ll Learn in FREE ML Course – Part 4 ✔ Real-world Machine Learning projects ✔ 40+ industry-level datasets ✔ Data preprocessing & feature engineering (hands-on) ✔ Model training, testing & evaluation ✔ Practical implementation of ML algorithms ✔ Real project challenges & error handling ✔ Resume-ready Machine Learning experience 💼 🔥 100% practical — no boring theory 🎯 Who Should Watch This Video? ✅ Beginners in Machine Learning ✅ Data Science & AI students ✅ Working professionals ✅ Python learners ✅ Anyone aiming for ML jobs in 2026 📌 Part 4 = Skill-Building Phase If you’ve completed Parts 1–3, this video will take your ML skills to a professional level 🚀 ⏱️ Timestamps: 00:02 – Overview of Previous Session & Dataset 01:11 – Dependent vs Independent Variables 02:08 – Identifying Target Variable (Price) 02:09 – Importing Data into Google Colab 03:20 – Manual File Upload Method 03:49 – Importing Essential Libraries 03:54 – Pandas Explained 04:16 – NumPy Explained 04:23 – Matplotlib & Seaborn for Visualization 04:57 – Importing sklearn Modules 05:43 – train_test_split Explained 06:35 – Pipeline Explained (McDonald’s Analogy) 08:20 – ColumnTransformer Explained 08:54 – Handling Categorical vs Numerical Data 10:14 – OneHotEncoder Explained 10:20 – StandardScaler Explained 11:54 – Linear Regression Problem: Outliers 13:40 – What Are Outliers? 15:56 – Impact on Model Performance 15:57 – Introduction to Regularization 16:16 – What is Regularization? 17:50 – Overfitting Explained 17:59 – Underfitting Explained 23:56 – Ridge Regression (L2 Regularization) 25:40 – Ridge Formula Explained 27:38 – How Ridge Handles Outliers 29:34 – Lasso Regression (L1 Regularization) 30:15 – Lasso Formula Explained 31:00 – How Lasso Shrinks Coefficients to Zero 32:53 – R² Score, MAE & MSE Mentioned 33:05 – Joblib: Saving Trained Models 33:57 – Benefits of Model Saving 34:05 – Reading Dataset with Date Parsing 35:10 – Using parse_dates in Pandas 📌▶️Playlist Links Here: Part 1 -    • FREE 🔥 Full Machine Learning Course 2026 😱...   Part 2 -    • FREE 🔥 Full Machine Learning Course – Part...   Part 3 -    • FREE 🔥 Full Machine Learning Course Part 3...   Part 4 -    • FREE 🔥 Machine Learning Course Part 4 😱 | ...   Part 5 -    • FREE Machine Learning Course Part 5 🔥 | RE...   Part 6 -    • FREE Machine Learning Course Part 6 🔥 | RE...   👉 LIKE 👍 | SHARE 🔁 | SUBSCRIBE 🔔 👉 Follow the channel for FREE certification & upcoming ML projects 📅 Updated for 2026 | 100% FREE | Practical Training Want Part 5 with ADVANCED ML + Live Projects? Comment 👉 ML Part 5 🚀 #MachineLearning #MachineLearningCourse #MachineLearningFree #MachineLearning2026 #MLCourse #learnmachinelearning #FreeMLCourse #PracticalMachineLearning #MLTraining #MLProjects #RealWorldML #HandsOnMachineLearning