DS Tree ASM 2 | Skillari

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2022 Machine Learning and AI Bootcamp: Zero to Hero

Overview

Overview (11:13)

Introduction

What is Data and why you should learn about it (4:43)
What is Machine Learning and its applications (10:11)
Lifecycle of Machine Learning (4:14)

Feature engineering

Feature Handling (8:48)
Handling Missing Data - Part 1 (6:45)
Handling Missing Data - Part 2 (7:06)
Handling Missing Data Notes
Handling Outliers - Part 1 (6:25)
Handling Outliers - Part 2 (7:57)
Handling Outliers Notes
Binning (9:30)
Binning Notes
Encoding - Part 1 (7:24)
Encoding - Part 2 (8:04)
Encoding Notes
Scalling - Part 1 (9:42)
Scalling - Part 2 (8:34)
Scalling Notes
Downloadable IPYNB File Notes

Machine Learning Terminologies

Introduction and Lifecycle (4:59)
Classification of Machine Learning - Supervised Algorithms (6:52)
Unsupervised Learning (8:53)
Reinforcement Learning (1:49)
Real World Examples (4:34)
Spliting data (8:02)
Train Test Split (4:41)
Split the data Notes
Downloadable IPYNB File Notes

Linear Regression - Regression Analysis

Regression Analysis - Part 1 (6:59)
Regression Analysis - Part 2 (9:42)

Linear regression - Univariate Regression

Univariate Regression Theory - Part 1 (7:32)
Univariate Regression Theory - Part 2 (5:06)
Univariate Regression Theory - Part 3 (6:03)
Univariate Regression Theory - Part 4 (7:22)
Univariate Regression Theory - Part 5 (7:02)
Univariate Regression using sklearn Theory (4:10)
Univariate Regression Lab - Part 1 (8:08)
Univariate Regression Lab - Part 2 (8:29)
Univariate Regression Lab - Part 3 (6:50)
Univariate regression - Evaluation metrics of Regression Lab (13:23)
Univariate Linear Regression Notes
Downloadable IPYNB File Notes

Linear Regression - Multivariate Regression

Multivariate Regression Theory - Part 1 (4:10)
Multivariate Regression Theory - Part 2 (5:39)
Multivariate Regression - Load Dataset Lab (8:10)
Multivariate Regression - EDA Lab (9:02)
Multivariate Regression - Fit Model Lab (7:56)
Multivariate Linear regression Notes
Downloadable IPYNB File Notes

Linear Regression - Polynomial Regression

Polynomial Regression Theory (6:27)
Polynomial Regression Lab- Part 1 (7:46)
Polynomial Regression Lab- Part 2 (5:15)
Polynomial Regression Notes
Downloadable IPYNB File Notes

Linear Regression - Decision Tree Regression

Decision Tree Introduction Theory (8:59)
Decision Tree Regression Theory (8:55)
Random Forest Regression Theory (7:26)
Decision Tree Regression Lab (8:48)
Visualize decision tree Regressor Lab (9:21)
Random forest Regressor Lab (6:28)
Decision Tree and Random Forest Regression Notes
Downloadable IPYNB File Notes

Linear regression - Hyperparameter Tuning

HyperParameter Intuition (7:11)
RF Regressor (9:41)
Grid Search (8:13)
Random Search (5:43)
HyperParameter tuning Notes
Downloadable IPYNB File Notes

Regression Project

Problem (5:22)
Regression Project
Solution 1 (7:26)
Solution 2 (4:47)
Solution 3 (8:46)
Solution 4 (7:46)
Regression Project - Solutions
Downloadable IPYNB File Notes

Model Diagnotics

Model Diagnostics (6:22)
Bias and Variance (11:41)
Tradeoff between Bias and Variance (7:32)
Overfitting and Underfitting (11:11)

Classification

Classification Problems (5:36)
Confusion Matrix (7:07)
Metrics (10:20)
Practice (5:11)

Logistic Regression

Logistic Regression Intro (6:32)
Logistic Regression Maths intuitions Theory - Part 1 (5:26)
Logistic Regression Maths intuitions Theory - Part 2 (9:17)
Logistic Regression Maths intuitions Theory - Part 3 (7:29)
Logistic Regression Lab - Part 1 (5:07)
Logistic Regression Lab - Part 2 (8:22)
Logistic Regression Lab - Part 3 (10:19)
Logistic Regression Lab - Part 4 (8:07)
Logistic Regression Lab - Part 5 (6:10)
Logistic Regression (optional) (6:09)
Logistic Regression Notes
Downloadable IPYNB File Notes

KNN

KNN Intro (6:21)
KNN Algo Intuition (6:28)
KNN Distance Functions (9:42)
Finding K (6:33)
Problem Understanding Lab (5:52)
Scalling and Spliting Data Lab (6:40)
Training our first KNN model (5:56)
Finding optimal value of k (9:34)
KNN Notes
Downloadable IPYNB File Notes

SVM

Overview (8:40)
SVM Types (3:00)
Import Dataset (5:45)
EDA 1 (9:24)
EDA 2 (12:51)
Encoding (7:28)
Training (4:34)
Prediction and Evaluation (6:58)
SVM Notes
Downloadable IPYNB File Notes

Decision Tree

DS Tree Intro (9:12)
DS Tree Algo (9:12)
DS Tree ASM 1 (5:36)
DS Tree ASM 2 (5:35)
DS Tree Advantage and Disadvantage (4:03)
DS Load Dataset (7:25)
DS EDA (9:17)
DS Prediction and Evaluation (8:18)
DS Model Training (7:34)
Visualization Model (7:13)
Decision Tree Notes
Downloadable IPYNB File Notes

Random Forest

RFC Intro (7:50)
RFC Deep (4:48)
Import and EDA (7:31)
Model Training and Evaluation (7:10)
Random Forest Notes
Downloadable IPYNB File Notes

Classification Project

Problem Statement (3:38)
Classification project Notes
Problem Solution - Part 1 (12:59)
Problem Solution - Part 2 (7:27)
Classification project - Solution Notes
Downloadable IPYNB File Notes

K-means Clustering

Clustering Intro (10:25)
K-means clustering - Part 1 (8:25)
K-means clustering - Part 2 (7:57)
Elbow Method (6:56)
K-means Lab - Part 1 (4:39)
K-means Lab - Part 2 (5:39)
K-means Lab - Part 3 (5:51)
K-means clustering Notes
Downloadable IPYNB File Notes

PCA

PCA Theory - Part 1 (5:38)
PCA Theory - Part 2 (7:34)
PCA Theory - Part 3 (4:23)
PCA Theory - Part 4 (10:09)
PCA Lab - Part 1 (8:22)
PCA Lab - Part 2 (9:47)
PCA Lab - Part 3 (8:41)
Principal Component Analysis Notes
Downloadable IPYNB File Notes

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