Machine Learning from scratch through Python

Lecture 1 1 lecture 15:24 Introduction to AI Introduction to Artificial Intelligence and its subsets Machine Learning and Deep Learning with Curriculum and examples. Lecture 1 1 lecture 15:24 Introduction to AI Introduction to Artificial Intelligence and its subsets Machine Learning and Deep Learning with Curriculum and examples. Introduction to AI Introduction to Artificial Intelligence and its subsets Machine Learning and Deep Learning with Curriculum and examples. Introduction to AI Introduction to Artificial Intelligence and its subsets Machine Learning and Deep Learning with Curriculum and examples. Introduction to AI Introduction to Artificial Intelligence and its subsets Machine Learning and Deep Learning with Curriculum and examples. Introduction to AI Introduction to Artificial Intelligence and its subsets Machine Learning and Deep Learning with Curriculum and examples. Introduction to Artificial Intelligence and its subsets Machine Learning and Deep Learning with Curriculum and examples. Introduction to Artificial Intelligence and its subsets Machine Learning and Deep Learning with Curriculum and examples. Lecture 2 1 lecture 37:45 Supervised and UnSupervised Learning Lecture 2 1 lecture 37:45 Supervised and UnSupervised Learning Supervised and UnSupervised Learning Supervised and UnSupervised Learning Supervised and UnSupervised Learning Supervised and UnSupervised Learning Lecture 3 1 lecture 01:00:54 KNN (Lp Norms) Lecture 3 1 lecture 01:00:54 KNN (Lp Norms) KNN (Lp Norms) KNN (Lp Norms) KNN (Lp Norms) KNN (Lp Norms) Lecture 4 1 lecture 57:57 KNN (Euclidean and Manhattan Distance) Lecture 4 1 lecture 57:57 KNN (Euclidean and Manhattan Distance) KNN (Euclidean and Manhattan Distance) KNN (Euclidean and Manhattan Distance) KNN (Euclidean and Manhattan Distance) KNN (Euclidean and Manhattan Distance) Lecture 5 1 lecture 59:31 KNN ( Minkowski, Hamming and Cosine Distance ) Lecture 5 1 lecture 59:31 KNN ( Minkowski, Hamming and Cosine Distance ) KNN ( Minkowski, Hamming and Cosine Distance ) KNN ( Minkowski, Hamming and Cosine Distance ) KNN ( Minkowski, Hamming and Cosine Distance ) KNN ( Minkowski, Hamming and Cosine Distance ) Lecture 6 1 lecture 01:00:10 Over and Under Fitting ( Cross Validation and K-Fold CV ) Lecture 6 1 lecture 01:00:10 Over and Under Fitting ( Cross Validation and K-Fold CV ) Over and Under Fitting ( Cross Validation and K-Fold CV ) Over and Under Fitting ( Cross Validation and K-Fold CV ) Over and Under Fitting ( Cross Validation and K-Fold CV ) Over and Under Fitting ( Cross Validation and K-Fold CV ) Lecture 7 1 lecture 55:32 Project 1 ( creating our first Model and finding the accuracy of it) Lecture 7 1 lecture 55:32 Project 1 ( creating our first Model and finding the accuracy of it) Project 1 ( creating our first Model and finding the accuracy of it) Project 1 ( creating our first Model and finding the accuracy of it) Project 1 ( creating our first Model and finding the accuracy of it) Project 1 ( creating our first Model and finding the accuracy of it) Lecture 8 1 lecture 53:11 Linear Regression Lecture 8 1 lecture 53:11 Linear Regression Linear Regression Linear Regression Linear Regression Linear Regression Lecture 9 1 lecture 35:48 Project 2 ( Simple Linear Regression ) Lecture 9 1 lecture 35:48 Project 2 ( Simple Linear Regression ) Project 2 ( Simple Linear Regression ) Project 2 ( Simple Linear Regression ) Project 2 ( Simple Linear Regression ) Project 2 ( Simple Linear Regression ) Lecture 10 1 lecture 45:50 Project 3 ( Multi - Linear Regression ) Lecture 10 1 lecture 45:50 Project 3 ( Multi - Linear Regression ) Project 3 ( Multi - Linear Regression ) Project 3 ( Multi - Linear Regression ) Project 3 ( Multi - Linear Regression ) Project 3 ( Multi - Linear Regression ) Lecture 11 1 lecture 01:02:01 HYPOTHESIS TESTING ( Statistics Fundamentals ) Lecture 11 1 lecture 01:02:01 HYPOTHESIS TESTING ( Statistics Fundamentals ) HYPOTHESIS TESTING ( Statistics Fundamentals ) HYPOTHESIS TESTING ( Statistics Fundamentals ) HYPOTHESIS TESTING ( Statistics Fundamentals ) HYPOTHESIS TESTING ( Statistics Fundamentals ) Lecture 12 1 lecture 51:11 Decision Tree Part with Gini Index Lecture 12 1 lecture 51:11 Decision Tree Part with Gini Index Decision Tree Part with Gini Index Decision Tree Part with Gini Index Decision Tree Part with Gini Index Decision Tree Part with Gini Index Lecture 13 1 lecture 39:39 Decision Tree with Information Gain Lecture 13 1 lecture 39:39 Decision Tree with Information Gain Decision Tree with Information Gain Decision Tree with Information Gain Decision Tree with Information Gain Decision Tree with Information Gain Lecture 14 1 lecture 01:40:18 Project 4 ( Decision Tree ) Lecture 14 1 lecture 01:40:18 Project 4 ( Decision Tree ) Project 4 ( Decision Tree ) Project 4 ( Decision Tree ) Project 4 ( Decision Tree ) Project 4 ( Decision Tree )