GitHub - satwiksps/Machine_Learning: This project is a hands-on application of the Python programmes presented in Jason Brownlee's Machine Learning Mastery With Python book

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This project is a hands-on application of the Python programmes presented in Jason Brownlee's Machine Learning Mastery With Python book

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Name		Name	Last commit message	Last commit date
Latest commit History 44 Commits
PyTorch_ML		PyTorch_ML
01_ML_Libraries.ipynb		01_ML_Libraries.ipynb
02_Data_Statistics.ipynb		02_Data_Statistics.ipynb
03_Data_Visualisation.ipynb		03_Data_Visualisation.ipynb
04_Data_Pre_Processing.ipynb		04_Data_Pre_Processing.ipynb
05_Feature_Selection.ipynb		05_Feature_Selection.ipynb
06_Evaluating_Performance.ipynb		06_Evaluating_Performance.ipynb
07_Performance_Metrics.ipynb		07_Performance_Metrics.ipynb
08_Classification_Algorithms.ipynb		08_Classification_Algorithms.ipynb
09_Regression_Algorithms.ipynb		09_Regression_Algorithms.ipynb
10_Comparing_ML_Algorithm.ipynb		10_Comparing_ML_Algorithm.ipynb
11_Pipeline_Automation.ipynb		11_Pipeline_Automation.ipynb
12_Save_N_Load_ML_Models.ipynb		12_Save_N_Load_ML_Models.ipynb
readme.txt		readme.txt

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Hello World
This repository contains a collection of resources, code samples, and projects aimed at helping beginners get started with machine learning.
Whether you're new to programming or looking to expand your skills into the field of machine learning, you'll find valuable content here to guide you on your journey.

Before you begin, you should have a basic understanding of:
1) Python programming language
2) Basic mathematics (linear algebra, statistics)

Some of the projects include:
1) Linear Regression: Predicting housing prices
2) Gradient Descent And Cost Function
3) Classification: Classifying handwritten digits using the MNIST dataset
4) Logistic Regression
5) Clustering: Grouping similar data points using k-means clustering
6) Bias Variance
7) More on the way