Scikit-Learn Course - Machine Learning in Python Tutorial

Overview

This course on machine learning using Scikit-learn in Python aims to teach learners the fundamentals of machine learning, various algorithms such as KNN, SVM, linear regression, and neural networks, as well as concepts like overfitting, underfitting, and backpropagation. By the end of the course, students will be able to install Scikit-learn, plot graphs, work with features and labels, understand different machine learning algorithms, and implement models for classification, regression, and clustering. The course employs a tutorial-based teaching method with hands-on coding examples. It is suitable for beginners interested in machine learning and Python programming.

Syllabus

Introduction.
Installing SKlearn.
Plot a Graph.
Features and Labels_1.
Save and Open a Model.
Classification.
Train Test Split.
What is KNN.
KNN Example.
SVM Explained.
SVM Example.
Linear regression.
Logistic vs linear regression.
Kmeans and the math beind it.
KMeans Example.
Neural Network.
Overfitting and Underfitting.
Backpropagation.
Cost Function and Gradient Descent.
CNN.
Handwritten Digits Recognizer.