Complete Machine Learning course with Math in Hindi

Machine Learning notes-
https://ownstreet.in/datadissection/shop

Probability and statistics notes, link-
https://ownstreet.in/datadissection/shop

Telegram group- https://t.me/data_dissection
Neural Networks code file- https://drive.google.com/drive/folders/1MQcgG9WEfnrbMpG4sr9AoAZo8djcBPV2?usp=sharing

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 one shot
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.

00:00:00 - Introduction to course
00:01:12 - Introduction to Probability
00:03:12 - Probability definition
00:14:19 - Set theory
00:22:20 - Fair events
00:25:39 - Conditional Probability
00:36:46 - Types of events
00:43:32 - Bayes Theorem
01:00:50 - Combinatorics
01:32:05 - Random variables
01:37:01 - Mean, Median and Mode
01:45:22 - Standard Deviation
01:53:46 - Expectation
02:06:04 - Covariance
02:18:22 - Correlation
02:28:43 - Binomial Distribution
02:34:39 - Probability Distribution Functions
02:55:36 - Normal Distribution and Z-score
03:08:23 - Microsoft Data Science interview quesn
03:19:12 - PhonePe interview quesn
03:25:55 - Statistics, Population and samples
03:38:09 - Central Limit Theorem
03:50:32 - QQ plot
03:57:02 - QQ plot code
04:01:58 - Hypothesis Testing(Null hypothesis)
04:18:32 - 2 tailed test
04:34:09 - Z Test
04:45:46 - T Test
04:55:30 - 2 sample test
05:09:49 - KS Test
05:22:05 - Chi square test
05:35:19 - ANOVA
05:45:38 - F Test
05:52:34 - Introduction to Linear Algebra
05:54:07 - Straight lines
06:01:07 - Co-ordinate geometry
06:17:23 - Vectors
06:29:40 - Matrix
06:41:35 - Determinants
06:49:00 - Eigen value & Eigen vectors
06:54:02 - Introduction to calculus
06:56:50 - Functions
07:08:57 - More on functions
07:37:28 - Limits and deriving dy/dx
07:49:32 - Rules of dy/dx
07:56:32 - Application of dy/dx
08:20:25 - L-0 intro
08:23:14 - L-0 Machine learning full course
08:30:15 - L-1 Gradient Descent
08:47:38 - L-2 Linear Regression (Math)
09:05:20 - L-3 Coefficient of determination
09:13:12 - L-4 Multiple Linear Regression
09:33:44 - L-5 Assumptions behind Linear Regression
09:46:40 - L-6 Polynomial Linear Regression
10:02:11 - L-7 Bias Variance Tradeoff
10:17:17 - L-8 Lagrange Multiplier and Hyperparameter
10:35:57 - L-9 Logistic Regression
10:50:22 - L-10 Machine learning Mini project
11:14:42 - L-11 Precision and Recall
11:27:03 - L-12 KNN Algorithm
11:37:57 - L-13 Bias-Variance in KNN
11:45:31 - L-14 Cosine similarity and other distances
11:53:19 - L-15 S.M.O.T.E (for KNN)
12:00:20 - L-16 Decision Tree (Part-1)
12:18:30 - L-17 Decision Tree (Part-2)
12:29:50 - L-18 Hyperparameter in Decision Tree
12:38:26 - L-19 Ensemble models and Random forest
12:54:45 - L-20 Boosting based algorithms
13:06:51 - L-21 Machine learning project
13:27:43 - L-22 Area Under ROC curve
13:41:24 - L-23 Support Vector machines SVM
13:55:14 - L-24 SVM Primal Dual Form
14:06:50 - L-25 RBF Kernel in SVM
14:12:41 - L-26 Naive Bayes Classifier
14:23:10 - L-27 Fine-Tuning a Naive bayes model
14:32:50 - L-28 PCA principal component analysis
14:55:55 - L-29 Unsupervised machine learning
15:08:38 - L-30 K-Means Clustering
15:30:48 - L-31 Hierarchical Clustering
15:42:26 - L-32 DBSCAN
16:06:39 - Neural Networks intro
16:13:34 - L-33 Perceptron in Neural-Networks
16:24:27 - L-34 Multicalss classification and Softmax
16:32:51 - L-35 Backpropagation for Neural networks
16:50:44 - L-36 Building Neural networks from scratch (Part-1)
17:07:00 - L-37 Building Neural networks from scratch (Part-2)
17:23:13 - L-38 Coding Neural Network from scratch (Part-3)
17:46:10 - L-39 Basics of Tensorflow and Keras
17:57:15 - L-40 Optimizing a Neural Network
18:19:12 - L-41 RMS and ADAM Optimizer
18:31:22 - L-42 Regularization and Drop Out
18:48:31 - L-43 Data Science project using Neural Networks