Dive into machine learning with Python using scikit-learn in this comprehensive tutorial. Master essential concepts and techniques, from installation and data visualization to advanced algorithms. Explore classification, KNN, SVM, linear and logistic regression, and K-means clustering. Delve into artificial intelligence fundamentals, including neural networks, overfitting and underfitting, backpropagation, cost functions, and gradient descent. Apply your knowledge to real-world projects like handwritten digit recognition using CNNs. Gain hands-on experience with practical examples, code implementations, and in-depth explanations of machine learning principles. Perfect for beginners and intermediate learners looking to enhance their Python-based machine learning skills.
Overview
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.
Taught by
freeCodeCamp.org
