🚀 Part 1 Machine Learning Full Course for Beginners to Pros Intuitive ML Explained with Python

Welcome to the Ultimate Machine Learning Full Course — designed for both beginners and professionals who want to master machine learning in an intuitive, visual, and hands-on way ! 🚀 Chapters: 0:00:00 ML Roadmap 0:13:30 Intro Understand Machine Learning 0:28:20 Set Up Your Python env 0:37:22 Linear Regression Made Easy 1:13:02 Linear Regression Metrics 1:21:55 Polynomial Regression with Gradient Descent 1:31:42 Logistic Regression 1:50:29 Decision Trees 2:05:55 Naïve Bayes 2:19:30 Support Vector Machine (SVM) 2:36:26 Multi-Class SVM 2:43:25 K-Nearest Neighbors KNN 2:55:05 Binary Classification 3:13:17 ROC Curve & AUC 3:27:14 Binary Classification 2 3:48:02 Multi-Class Classification 3:54:58 K-Means Clustering 4:10:29 Hierarchical Clustering 4:23:25 Clustering Metrics 4:33:06 PCA 4:47:42 Incremental PCA 5:00:09 🌳 Random Forest 5:11:09 Random Forest Scalable ML Pipeline 5:16:48 AdaBoost 5:28:49 AdaBoost Integration into Sysytem 5:34:10 Gradient Boosting Regression 5:43:33 Gradient Boosting Classifier 5:49:59 XGBoost Classification 6:08:20 XGBoost Integration in a Real ML Pipeline 6:14:57 XGBoost Regression 6:28:05 Cross-Validation 6:37:58 Overfitting vs Underfitting 6:51:19 Neural Networks 7:12:22 Backpropagation 7:14:34 Loss Functions 7:22:42 Calculate Parameters of a Neural Network 7:29:00 CNNs 7:43:51 How CNN Filters Work 7:51:35 CNN Output Shape 7:52:56 Convolutional Layers 8:01:45 Pooling Layers 8:09:51 Activation Functions 8:17:13 VGG16 Explained with PyTorch 8:59:54 Training VGG16 with PyTorch 9:32:50 Batch Normalization 9:49:35 Layer Normalization 9:57:27 Transfer Learning 10:35:03 Feature Extraction with ResNet18 11:09:37 1x1 Convolution Whether you're just starting out or looking to deepen your understanding, this course breaks down complex concepts into simple ideas using: ✅ Clear explanations ✅ Numerical examples ✅ Python implementations ✅ Step-by-step coding walkthroughs ✅ Real-world projects 📚 What You'll Learn: 🔹 Foundations of Machine Learning Types of ML, setup environment, Jupyter Notebooks, core libraries like NumPy, Pandas, Scikit-learn. 🔹 Supervised Learning Linear Regression, Logistic Regression, Decision Trees, SVM, KNN — all with numerical examples and Python code. 🔹 Unsupervised Learning Clustering with K-Means, Hierarchical Clustering, Dimensionality Reduction using PCA and Incremental PCA for big data. 🔹 Ensemble Methods Random Forests, AdaBoost, Gradient Boosting, XGBoost — plus professional-grade code organization using inheritance , abstract classes , and polymorphism . 🔹 Deep Learning Basics Neural Networks, CNNs, Batch Normalization, Layer Normalization — with PyTorch implementations. 🔹 Model Optimization & Evaluation Cross-validation, overfitting, underfitting, performance metrics like RMSE, MAE, F1-score, ROC-AUC, and more. 🔹 Real-World Projects Spam detection, customer segmentation, house price prediction — even deploying models with Streamlit ! 🎥 Every concept is backed by a dedicated YouTube video , and all code is available in the GitHub repository so you can follow along step by step. 🔗 Code & Resources: 👉 GitHub Repo: https://github.com/DeepKnowledge1/ml 👉 Machine Learning Course Playlist: https://www.youtube.com/playlist?list=PL-kVqysGX5179csIx8Ujesglg6tNll9LI 📌 Don’t forget to: 👍 Like the video if you found it helpful 🔔 Subscribe for more intuitive AI/ML content 💬 Drop your questions in the comments — we’re here to help! 🔖 Hashtags (SEO optimized, lowercase): #machinelearning #deeplearning #python #datascience #mlcourse #machinelearningtutorial #mlengineer #artificialintelligence #beginnerfriendly #coding #neuralnetworks #xgboost #randomforest #svm #kmeansclustering #pca #incrementalpca #batchnormalization #layernormalization #pytorch #jupyternotebook #scikitlearn #numpy #pandas #streamlit #projectbasedlearning #machinelearningprojects #mlforbeginners #advancedml