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Awesome Dive into Machine Learning

Dive into Machine Learning with Python Jupyter notebook and scikit-learn!

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Last Update: Oct. 20, 2021, 12:03 p.m.

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Dive into Machine Learning

My coworkers often ask me for Python learning resources. Here are some picks. Many skill levels, emphasis on beginner and intermediate.

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Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead."

Local installation

Python 3 is the best option.

Notebook and project application for interactive data science and scientific computing across all programming languages.

Cloud-based

A curated list of awesome Jupyter projects, libraries and resources

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🏆 A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools). Updated weekly.

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Enable sharing and execute Jupyter Notebooks

free jupyter notebook online. Google Colab also comes with free GPU hours.

Let's go!

A Few Useful Things to Know about Machine Learning

Quoting Domingos: "Suppose you’ve constructed the best set of features you can, but the classifiers you’re getting are still not accurate enough. What can you do now? There are two main choices: design a better learning algorithm, or gather more data. [...] As a rule of thumb, a dumb algorithm with lots and lots of data beats a clever one with modest amounts of it. (After all, machine learning is all about letting data do the heavy lifting.)"

Jargon note

Just about time for a break...

episode, and listen to that soon.** It supports what we read from Domingos. Ryan Adams talks about starting simple, as we discussed above. Adams also stresses the importance of feature engineering. Feature engineering is an exercise of the "knowledge" Domingos writes about. In a later episode, they share many concrete tips for feature engineering.

Play to learn

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

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Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

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Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.

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Using Titanic data, "Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques."

Machine Learning](https://www.coursera.org/learn/machine-learning) is a popular and esteemed free online course. I've seen it recommended often. And emphatically.**

Tips for studying

videos. This is just about how to study in general. In the course, they advocate the learn-by-doing approach, as we're doing here. You'll get various other tips that are easy to apply, but go a long way to make your time investment more effective.

Other courses

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

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the best machine learning tutorials on the web

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starts with what we've already covered, then continues on at a comfortable place. After the videos you could do Markham's General Assembly's Data Science course. Interactive. Markham's course is also offered in-person in Washington, DC.

online course based on Data 8 is now offered via edX too.

Includes Coursera's Data Science Specialization with 9 courses in it. The Specialization certificate isn't free, but you can take the courses 1-by-1 for free if you don't care about the certificate. The survey also covers Harvard CS109 which I've seen recommended elsewhere.

Supplement: Learning Pandas well

Supplement: Cheat Sheets

More Data Science materials

Bayesian Statistics and Machine Learning

A python tutorial on bayesian modeling techniques (PyMC3)

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A machine learning / bayesian inference engine assigning attributes to objects

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_ Made with a "computation/understanding-first, mathematics-second point of view." It's available in print too!

Risks

Welcome to the Danger Zone

Towards Expertise

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

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Ask for Peer Review

It's dangerous to go alone, take these!

This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.

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A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

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This repository provides a curated list of references about Machine Learning Model Governance, Ethics, and Responsible AI.

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A curated list of references for MLOps

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:bow: A note about Machine Learning and User Experience (UX)

Deep Learning

ARCHIVED: Contains historical course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

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An interactive book about deep learning

courses on Deep Learning](https://www.coursera.org/specializations/deep-learning)!** There five courses, as part of the Deep Learning Specialization on Coursera. These courses are part of his new venture, deeplearning.ai

Google's fast-paced, practical introduction to machine learning.

Yet halfway through the book, it contains satisfying math content on how to think about actual deep learning.

answered by Greg Brockman (Co-Founder & CTO at OpenAI, previously CTO at Stripe)

"Big" Data?

Finding Open-Source Libraries

A curated list of awesome Machine Learning frameworks, libraries and software.

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Curated decibans of scientific programming resources in Python.

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Lore makes machine learning approachable for Software Engineers and maintainable for Machine Learning Researchers

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Curated decibans of Julia programming language.

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TensorFlow is an Open Source Software Library for Machine Intelligence

Alternative ways to "Dive into Machine Learning"

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

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

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by Sebastian Raschka. A selection of the core online courses and books for getting started with machine learning and gaining expert knowledge. It contextualizes Raschka's own book, Python Machine Learning (which I would have linked to anyway!) See also pattern_classification GitHub repository maintained by the author, which contains IPython notebooks about various machine learning algorithms and various data science related resources.

Google's fast-paced, practical introduction to machine learning.

Amazon have open up their internal training to the public and also offer certification. 30 courses - 45+ hours of content.

is another good introduction, perhaps better if you're more familiar with Java or Scala. It introduces machine learning for a developer audience using Smile, a machine learning library that can be used both in Java and Scala.

is a journal devoted to clear and interactive explanations of the lastest research in machine learning. They offer an alternative to traditional academic publishing that promotes accessibility and transparency in the field.