Complete Machine Learning in Hindi

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Full Machine Learning course in hindi- https://youtube.com/playlist?list=PLlpUUtQ9RrF4o3UTYbc4cP3NCEyE5BwX5&si=RVj6gVjE2J0XwPw8
Full Neural networks course- https://youtube.com/playlist?list=PLlpUUtQ9RrF47CpLH6PSFL2oeP3izPMhE&si=xJ5E79pugCDkYxR7
Math for ai
math for machine learning math for data science probability for machine learning probability for data science machine learning machine learning roadmap machine learning full course machine learning projects machine learning engineer roadmap machine learning tutorial machine learning playlist machine learning course machine learning interview questions machine learning machine learning full course in hindi machine learning in tamil machine learning in telugu machine learning with python Welcome to this comprehensive Machine Learning Course in Hindi . In this playlist, you’ll master the essential machine learning algorithms with hands-on coding tutorials, real-world examples, and step-by-step explanations. Whether you’re a beginner or looking to enhance your skills, this course covers everything you need to know about Machine Learning from scratch. Perfect for aspiring data scientists, AI enthusiasts, and anyone interested in the field of artificial intelligence.In this series, we will dive into:Linear Regression for predicting continuous values Logistic Regression for classification problems Decision Trees and Random Forests for powerful decision-making models k-Nearest Neighbors (k-NN) for intuitive classification Support Vector Machines (SVM) for high-dimensional data Naive Bayes for probabilistic classifiers K-Means Clustering for unsupervised learning PCA (Principal Component Analysis) for dimensionality reduction Gradient Descent and optimization techniques Neural Networks basics and deep learning introduction Cross-validation techniques for model validation Hyperparameter tuning for improving model performance By the end of this course, you’ll have a solid understanding of machine learning fundamentals and be able to implement these algorithms confidently in your projects using Python, scikit-learn, and other popular ML libraries
Build Neural Networks from scratch using Python and TensorFlow
Understand key topics like Backpropagation, Gradient Descent, and Activation Functions
Apply Neural Networks to real-world problems in AI, Machine Learning, and Data Science
With hands-on projects, quizzes, and clear explanations, you’ll go from theory to implementation in no time! Whether you’re a student, a professional, or a tech enthusiast, this course is designed to accelerate your learning and boost your AI career.
Subscribe for more tutorials on AI, Machine Learning, and Data Science Join now and start mastering Neural Networks!

00:00:00 0- intro
00:02:49 L-0 Machine learning full course
00:09:50 L-1 Gradient Descent
00:27:13 L-2 Linear Regression (Math)
00:44:55 L-3 Coefficient of determination
00:52:47 L-4 Multiple Linear Regression
01:13:19 L-5 Assumptions behind Linear Regression
01:26:15 L-6 Polynomial Linear Regression
01:41:46 L-7 Bias Variance Tradeoff
01:56:52 L-8 Lagrange Multiplier and Hyperparameter
02:15:32 L-9 Logistic Regression
02:29:57 L-10 Machine learning Mini project
02:54:17 L-11 Precision and Recall
03:06:38 L-12 KNN Algorithm
03:17:32 L-13 Bias-Variance in KNN
03:25:06 L-14 Cosine similarity and other distances
03:32:54 L-15 S.M.O.T.E (for KNN)
03:39:55 L-16 Decision Tree (Part-1)
03:58:05 L-17 Decision Tree (Part-2)
04:09:25 L-18 Hyperparameter in Decision Tree
04:18:01 L-19 Ensemble models and Random forest
04:34:20 L-20 Boosting based algorithms
04:46:26 L-21 Machine learning project
05:07:18 L-22 Area Under ROC curve
05:20:59 L-23 Support Vector machines SVM
05:34:49 L-24 SVM Primal Dual Form
05:46:25 L-25 RBF Kernel in SVM
05:52:16 L-26 Naive Bayes Classifier
06:02:45 L-27 Fine-Tuning a Naive bayes model
06:12:25 L-28 PCA principal component analysis
06:35:30 L-29 Unsupervised machine learning
06:48:13 L-30 K-Means Clustering
07:10:23 L-31 Hierarchical Clustering
07:22:01 L-32 DBSCAN
07:46:14 Neural Networks intro
07:53:09 L-33 Perceptron in Neural-Networks
08:04:02 L-34 Multicalss classification and Softmax
08:12:26 L-35 Backpropagation for Neural networks
08:30:19 L-36 Building Neural networks from scratch (Part-1)
08:46:35 L-37 Building Neural networks from scratch (Part-2)
09:02:48 L-38 Coding Neural Network from scratch (Part-3)
09:25:45 L-39 Basics of Tensorflow and Keras
09:36:50 L-40 Optimizing a Neural Network
09:58:47 L-41 RMS and ADAM Optimizer
10:10:57 L-42 Regularization and Drop Out
10:28:06 L-43 Data Science project using Neural Networks

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