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Machine Learning
Section 1 : Machine Learning Basics and its Life Cycle
Lecture 1 : Machine Learning introduction part 1 : difference Between Business Intelligence Team, Data Analyst , and Data Scientist, And Purpose of Machine Learning, Deep Learning, NLP, and AI (71:13)
Lecture 2 : What is Machine Learning ? Introduction to Supervised Learning and Unsupervised Learning. (54:50)
Lecture 3 : Introduction to Reinforcement learning . how traffic board , bank fraud transaction systems can use machine learning. (44:39)
Lecture 4 : 3 types of data sets used in Machine Learning . And 3 approaches to create Train , Validation, test data sets as part of data preperation (41:31)
Lecture 5 : Machine Learning Life cycle part 1 --> introduction to data extractions . And more details on online, batch, data streaming systems. (52:34)
Lecture 6 : Machine Learning Life Cycle Part 2 --> Introduction to NOSQL database sources . Types of NoSql databases. Overview of Key value store databases, document store databases, Columner Store Databases, Graph Store Databases (63:07)
Lecture 7 : Machine Learning Life Cycle Part 3 --> Data Cleansing and Transormations . How to clean missed values. How to clean missed values if problem is Regression Problem. (46:11)
Lecture 8 : Machine Learning Life Cycle Part 4 : --> Data cleansing and Transformations > how to transform if input variable is character(string) continues value. Need of Scaling data and Scaling Techniques. When to use what type of scaling technique. Introduction to Training model, Evaluating model, Model Selection, Deployment of model (52:19)
Lecture 9 : Machine Learning Life Cycle Part 5 --> Rebuilding a model , and Summary of Machine Learning Life Cycle. (26:24)
Lecture 10 : A Bonus Session : More on supervised, unsupervised, Reinforcement Learning (53:09)
Lecture 11 : Preparing Train and Test sets Using Python and numpy (28:25)
Lecture 12 cleaning missed values in continuous variables with python for Regression models (40:01)
Lecture 13 How to transform if input variable is character categorical(classifier) for regression model (15:30)
Lecture 14 : Transforming string continuous variables into numerical scores Using Python and numpy (25:03)
Lecture 15 : Transforming String categorical values into probabilities with random noise using Python and numpy for regression models (35:30)
Lecture 16 Scaling input features and labels for Regression models Using Python and Numpy (50:17)
Section 2 : Machine Learning Models introduction, Tensorflow Basics , Pytorch Basics
Lecture 17 : Introduction to types of models such as predictive models, clustering models , Recommender Systems models . And difference between predictions and forcasting (45:07)
Lecture 18 : Introduction to clustering models and Recommendation system models (25:37)
Lecture 19 : Introduction to Linear Regression. How to Derive coefficients which will correlate input features and target labels (41:37)
Lecture 20 : More discussion on Linear Regression. How to Deal non linear regression using Polynomial techniques such as quadratic models and cubic models (36:49)
Lecture 21 Implementation of Linear, Qudratic polynomial, Cubic Polynomial Regression Using R language (46:35)
Lecture 22 Developing predict function and Accuracy testing function using R Language (34:05)
Lecture 23 Statistical approach of Tuning Co-efficients . Problems in Statistical approach . And a Little Introduction to Gradient Descent Algorithm (40:31)
Lecture 24 : How to prepare Linear input Matrix And How to Convert Linear Matrix into n-degree-polynomial matrix using Python Numpy (57:39)
Lecture 25 : Implementation of Linear and polynomial regressions . And accuracy testing. And predicting target labels of new data Using Python Numpy (92:55)
Lecture 26 : Introduction to Tensorflow (50:05)
Lecture 27 : A sample code explanation with Tensorflow and Keras (50:11)
Lecture 28 : Step by Step explanation of Tensorflow code part 1 (57:12)
Lecture 29 : Step By Step explanation of Tensorflow code Part 2 (52:59)
Lecture 30 : Step by Step Explanation of Tensorflow code Part 3 (35:59)
Lecture 31 : Low Level Api of Tensorflow Part 1 (52:40)
Lecture 32 : Low Level Api of Tensorflow Part 2 (86:07)
Lecture 33 : How to derive Weight matrix if you have multiple target variables (55:41)
Lecture 34 : Tensorflow Computations over matrices (45:29)
Lecture 35 : Extracting Weight matrix using Tensorflow for Linear Regression (37:50)
Section 3: Gradient Descent Algorithm
Lecture 36 (48:20)
Lecture 37 (41:11)
Lecture 38 (47:14)
Lecture 39 (25:35)
Lecture 40 (48:34)
Lecture 41 (33:44)
Lecture 42 (47:56)
Lecture 43 (28:16)
Section 4 : Classification Models and Logistic Regression
Lecture 44 (36:27)
Lecture 45 (37:09)
Lecture 46 (27:42)
Lecture 47 (33:40)
Section 5 : Naive Bayes Classification
Lecture 48 (38:12)
Lecture 49 (35:19)
Lecture 50 (28:59)
Lecture 51 (36:44)
Section 6 : Decision Tree Classifier
Lecture 52 (40:59)
Lecture 53 (35:01)
Lecture 54 (24:22)
Lecture 55 (44:06)
Lecture 56 (39:36)
Lecture 57 (39:19)
Section 7 : Random Forest classifier
Lecture 58 (30:34)
Lecture 59 (31:46)
Section 8 : Support Vector Machines Classifier
Lecture 60 (44:57)
Lecture 61 (45:44)
Lecture 62 (37:42)
Section 9 : Recommendation Systems
Lecture 63 (62:42)
Lecture 64 (34:36)
Lecture 65 (43:52)
Lecture 66 (47:29)
Lecture 67 (52:11)
Lecture 68 (58:45)
Lecture 69 (52:06)
Section 10 : KNN algorithm
Lecture 70 (56:34)
Lecture 71 (60:51)
Section 11 : R language essentials for Machine Learning
Lecture 72 (37:19)
Lecture 73 (56:39)
Lecture 74 (44:28)
Lecture 75 (47:15)
Lecture 76 (49:11)
Lecture 77 (43:02)
Lecture 78 (50:59)
Lecture 79
Lecture 80 (33:33)
Lecture 81 (49:40)
Lecture 82 (49:38)
Lecture 83 (53:13)
Lecture 19 : Introduction to Linear Regression. How to Derive coefficients which will correlate input features and target labels
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