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 β Score)
09:11:16 – Imbalanced dataset
09:40:15 – Naive Bayes
10:02:32 – Naive Bayes (practical)
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