About Course
Master the fundamentals of machine learning with this comprehensive course in Hindi by Code with Harry. Learn essential concepts like supervised and unsupervised learning, linear regression, logistic regression, and model evaluation. Gain hands-on experience through practical projects, including an end-to-end machine learning project and handwritten digit recognition using the MNIST dataset. Perfect for beginners eager to explore the world of machine learning with Python.
Course Content
Machine Learning With Python
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Why Learn Machine Learning?
05:56 -
Machine Learning Facts & Motivation
06:30 -
Features and Labels in Machine Learning
10:37 -
Data Collection for Machine Learning
11:15 -
Supervised and Unsupervised Learning
12:06 -
Installing Scikit-learn for ML
06:43 -
Training and Test Data Explained
09:35 -
Simple Linear Regression Explained
13:52 -
Multiple Regression Model Overview
12:55 -
Linear Regression Code in Python
26:20 -
How Linear Regression Works (Derivation)
18:10 -
Loss Functions and Gradient Descent
17:25 -
Mini-Batch and Stochastic Gradient Descent
12:57 -
Supervised Learning: Classification
12:18 -
K-Nearest Neighbor (KNN) Classification
12:48 -
KNN: Pros, Cons, and How It Works
15:11 -
Overfitting and Underfitting Explained
15:03 -
Logistic Regression: Overview and Working
22:40 -
Coding Logistic Regression in Python
49:35 -
End-to-End Python ML Project
03:06:19 -
Handwritten Digit Recognition Using MNIST Dataset
35:33 -
Precision, Recall, and F1-Score Explained
40:23 -
Evaluating Classifiers in Python
31:32
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