Machine Learning Full Course for Beginners (2024) | Learn Machine Learning in 10 Hours (Part-1)
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00:00:00 - ML Course Introduction
00:01:03 - What is Machine learning (ML)
00:17:19 - Complete Roadmap To Learn Machine Learning
00:26:16 - Types of Variables in Machine Learning
00:38:24 - Data Cleaning in Machine Learning
00:45:15 - What is missing value and how to find it
01:05:50 - Handling Missing Values (Dropping)
01:15:48 - Handling Missing Values (Imputing category data)
01:27:24 - Handling Missing Values (Scikit-Learn)
01:34:40 - One Hot Encoding & Dummy Variables
01:49:43 - What is Label Encoding?
01:58:16 - What is Ordinal Encoding?
02:12:02 - What is an Outlier and How to Handle It?
02:24:42 - How to Remove Outliers using IQR?
02:37:19 - How to Remove Outliers using Z Score?
02:56:18 - What is Feature Scaling (Standardization)?
03:16:32 - What is Feature Scaling (Normalization)?
03:27:06 - How to Handle Duplicate Data?
03:38:39 - How to Replace and Change Data Types?
03:47:28 - Function Transformer
04:05:17 - Backward Elimination (using MLxtend) & Forward Elimination (using MLxtend)
04:20:26 - Train Test Split in Data Set
04:31:19 - Regression Analysis
04:35:55 - Linear Regression Algorithm (Simple Linear)
04:47:19 - Linear Regression Algorithm (Simple Linear) Practical
05:13:39 - Multiple Linear Regression
05:34:46 - Polynomial Regression
05:55:33 - What is a cost function?
06:02:35 - Regression Cost Function - R Squared score & Adjusted R Squared Regression Analysis
06:18:18 - How to find a Best fit line?
06:32:38 - L1 (Lasso Regularization), L2 (Ridge Regularization) Theory
06:44:32 - L1 (Lasso Regularization), L2 (Ridge Regularization) Practical
07:06:32 - Classification
07:19:42 - Logistic Regression (practical) (Binary Classification)
07:40:51 - Logistic Regression (practical) (Binary Classification) (Multiple input)
07:53:57 - Logistic Regression (practical) (Binary Classification) (Polynomial input)
08:07:23 - Logistic Regression (practical) (Multiclass Classification)
08:23:31 - Confusion Matrix
08:46:47 - Confusion Matrix (Sensitivity, Precision, Recall, F1 - Score)
09:11:16 - Imbalanced dataset
09:40:15 - Naive Bayes
10:02:32 - Naive Bayes (practical)
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