Machine Learning with Python

Course Curriculum

Chapter 1: Machine Learning Introduction

Lecture 1: ML01_01_Machine Learning Introduction and Defination

Lecture 2: Ml02_01_ETP_Defimation

Lecture 3: ML03_01_Applications of ML

Lecture 4: ML04_01_Types of Machine Learning and Supervised Learning Introduction

Lecture 5: ML05_01_UnSupervised Learning Introduction

Lecture 6: ML06_01_reading _sklearn_ml_package_help_document part 1

Lecture 7: ML07_01_reading _sklearn_ml_package_help_document part 2

Lecture 8: ML08_01_Test Your Understanding

Chapter 2: Working with Datasets

Lecture 1: ML09_02_Explore Toy-Datasets

Lecture 2: ML10_02_Explore iris Dataset

Lecture 3: ML11_02_Similarly explore remaining toy datasets

Lecture 4: ML12_02_Create DataFrame from sklearn Bunch

Lecture 5: ML13_02_Create a Bunch with our own data

Lecture 6: ML14_02_Create a Bunch with our own data part 2

Chapter 3: k nearest neighbor algorithm

Lecture 1: ML15_03_k nearest neighbor algorithm Maths

Lecture 2: ML16_03_Find unknown sample quality based on known samples

Lecture 3: ML17_03_Find unknown flower name based on known flower names using MS excel

Lecture 4: ML18_03_Importance of n_neighbors

Lecture 5: ML19_03_Hamming distance

Chapter 4: KNN Estimator from Scratch

Lecture 1: ML20_04_KNN Estimator from Scratch

Lecture 2: ML21_04_Write code to Locate the most similar neighbors

Lecture 3: ML22_04_Write code to Make a classification prediction with neighbors

Lecture 4: ML23_04_High level End to End ML project Steps

Lecture 5: ML24_04_Load csv file and Understand X and y Data

Lecture 6: ML25_04_Split Data for training and testing

Lecture 7: ML26_04_Train or fit the model

Lecture 8: ML27_04_Predict labels of test data

Lecture 9: ML28_04_Accuracy_of_the_Clasification_model

Lecture 10: ML29_04_Hyper_Parameter_tunning

Lecture 11: ML30_04_k means cross validation

Lecture 12: ML31_04_GridSearchCV Hyper Parameter Tunning

Lecture 13: ML32_04_RandomizedSearchCV Hyper Parameter Tunning

Lecture 14: ML33_04_Save The model

Lecture 15: ML34_04_Load The model

Lecture 16: ML35_04_Home_Work

Chapter 5: Linear Regression

Lecture 1: ML36_05_Linear Regression Maths

Lecture 2: ML37_05_Find weight of the baby based on age data understanding

Lecture 3: ML38_05_Ordinary Least Squares

Lecture 4: ML39_05_Find parameters using Ordinary Least Squares Function

Lecture 5: ML40_05_Find parameters using sklearn

Lecture 6: ML42_05_Find parameters using covar and var

Lecture 7: ML43_05_Multivariate Linear Regression

Lecture 8: ML44_05_Linear_regression_to find life span based on number of fertilities part

Lecture 9: ML45_05_Linear_regression_to find life span based on number of fertilities part

Lecture 10: ML46_05_Supervised_Regression_Metric_R2_score

Lecture 11: ML47_05_Supervised_Regression_Metrics_RMSE

Lecture 12: ML48_05_Life Span Predication

Lecture 13: ML49_05_Linear Regression with Cross Validation or K-Fold

Lecture 14: ML50_05_Linear Regression with Boston dataset

Lecture 15: ML52_06_Logistic Regression Binary Clasification

Chapter 6: ML51_06_Logistic Regression Maths

Lecture 1: ML51_06_Logistic Regression Maths

Lecture 2: ML52_06_Logistic Regression Binary Clasification

Lecture 3: ML_53_06_Confusion Matrix

Lecture 4: ML_54_06_Classification Report

Lecture 5: ML_55_06_ROC Curve

Lecture 6: ML_56_06_AUC Computation

Chapter 7: Support Vector Machines Introduction

Lecture 1: ML_57_07_Support Vector Machines Introduction

Lecture 2: ML_58_07_Support Vectors and Maximizing the Margin

Lecture 3: ML_59_07_Non_linear_Support Vectors and Maximizing the Margin

Lecture 4: ML_60_07_upport Vector Machines Using Iris Toy Data set

Lecture 5: ML_61_07_Support_Vector_Machines_for_Face_Recognition

Chapter 8: Pre-processing of machine learning data Outliers

Lecture 1: ML_62_08_Pre-processing of machine learning data Outliers

Lecture 2: ML_63_08_Pre-processing of machine learning data Delete Outliers

Lecture 3: ML_64_08_Pre-processing Categorical Features

Lecture 4: ML_65_08_Pre-processing Categorical Features part2

Lecture 5: ML_66_08_Regression with categorical features using ridge algorithm

Lecture 6: ML_67_08_Dropping Missing Data

Chapter 9: ML_Pipeline with feature_selection and SVC

Lecture 1: ML_68_09_ML_Pipeline with feature_selection and SVC

Lecture 2: ML_69_09_ML_Pipeline with SimpleImputer and SVC

Chapter 10: Trees Entropy and Gini Maths Introduction

Lecture 1: ML_70_10_Trees Entropy and Gini Maths Introduction

Lecture 2: ML_73_10_Entropy Calculation using weather dataset part 3

Lecture 3: ML_74_10_Entropy Calculation using weather dataset part 4