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machine-learning-for-software-engineers

A complete daily plan for studying to become a machine learning engineer.

machine-learning-in-90-days

machine-learning-in-finance

Trading Strategy Development

machine-learning-links-and-lessons-learned

List of all the lessons learned, best practices, and links from my time studying machine learning

machine-learning-module

the best machine learning tutorials on the web

machine-learning-ng

The OG Machine Learning Coursera Course

machine-learning-notebooks

Machine Learning notebooks for refreshing concepts.

machine-learning-notes

My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(1000+页)和视频链接

machine-learning-project-walkthrough

An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!

machine-learning-projects

Machine Learning Experiments and Work

machine-learning-regression

Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

machine-learning-specialization

machine-learning-toolkits-with-python

Machine learning toolkits with Python

machine-learning-tutorials

machine learning and deep learning tutorials, articles and other resources

machine-learning-with-andrewng

Programming assignments for Coursera's Machine Learning Course.

machine-learning-with-python

Python code for common Machine Learning Algorithms

machine-learning-with-r

machine-learning-with-streamlit

Build a Machine Learning Web App with Streamlit and Python

machine_learning

Tutorials/tips on machine learning related topics

machine_learning_examples

A collection of machine learning examples and tutorials.

machine_learning_model_depoyment

Putting a model into production, from a Jupyter notebook to a fully deployed machine learning model, considering CI/CD, and deploying to cloud platforms and infrastructure.

machinelearning

Udacity course on Machine Learning

machinelearning-ensemblelearning

machinelearninginaction

Source Code for the book: Machine Learning in Action published by Manning

machinelearningnotebooks

Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft

machinelearningsamples-predictivemaintenance

MachineLearningSamples-PredictiveMaitenance

machinelearningwithpython

This repository contains implementation of the Various Machine Learning Algorithms like Supervised & Unsupervised Learning Introduction Regression and Classification Linear regression Logistic Regression Bias variance tradeoff / overfitting, regularization Cross validation and Support Vector Machines Decision trees Ensemble methods Neural Networks Neural Networks (cont.), Instance based learning Unsupervised Learning (Clustering: K-Means, Expectation Maximization) Randomized optimization, Dimensionality reduction, feature selection Feature transformation Reinforcement learning

master-thesis-wqu

check if sustainable investment strategies (SRI) lead to underperformace

mastering-exploratory-analysis-with-pandas

Mastering Exploratory Analysis with pandas, published by Packt

mathematics-for-machine-learning

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in maths - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialisation aims to bridge that gap, getting you up to speed in the underlying maths, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimise fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Components Analysis, uses the maths from the first two courses to do simple optimisation for the situation where you don’t have an understanding of how the data variables relate to each other. At the end of this specialisation you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.