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12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
This repository showcases a selection of machine learning projects undertaken to understand and master various ML concepts. Each project reflects commitment to applying theoretical knowledge to practical scenarios, demonstrating proficiency in machine learning techniques and tools.
The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI
Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, Large Language Models (LLMs), and Training Models.
🔔 No need to keep checking your training - just one import line and you'll know the second it's done.
Cross Beat (xbe.at) - Your hub for python, machine learning and AI tutorials. Explore Python tutorials, AI insights, and more.
This repository consists content, assignments, assignments solution and study material provided by ineoron ML masters course
Tutorial Series (60 hour course): Essentials of computer vision
Machine Learning Concepts with Concepts
The accompanying code for the book "Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits". A practical guide to implementing supervised and unsupervised machine learning algorithms in Python by Tarek Amr
A Deep-Learning multi-purpose chatbot made using Python3
📹 Collection of my Youtube Tutorials over the years! Read, write, and share what you create ✨🎈
Python codes from tutorials on the Data Professor YouTube channel
Clustering for mixed-type data
Resources about data science, machine learning, deep learning, data engineering, and SQL.
Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.
This is a repository built by the community for the community.