This post was originally published by Farhad Rahbarnia at Medium [AI]
Machine learning and AI courses and recourses for start-ups
Hi everyone, Today I want to talk about some resources that I have used or seen my entrepreneurs around me used to get familiar with AI.
Every day there are more and more start-ups that uses AI or build around them. AI has been around for almost 1950s and since 2008 with the introduction of AlexNets and deep learning, it became the hottest topic in Data science.
My journey to the Start-up world started with Data science degree. I finished my Master’s degree on parameter estimation for high dimensional signals (I am going to stop talking about it before I put you to sleep!). During this time I noticed people in start-ups were looking to either start programming Machine learning program or understand it enough so that they can explain it to their customer and development team.
Here I decided to summarize some of the books, courses and programs that I had come across over the years.
Before we dive down I’d like to breakdown some sections here. For Machine Learning programming I recommend using Python, however I have seen or used Julia, C++, R, and MATLAB. The reason why I like python is because, it is simple to learn, free and opensource, and it has a wonderful community with lots of libraries such as Pytorch and TensorFlow.
The topics that I will cover are big data, Deep Learning, Statistical Machine Learning, and Reinforcement Learning.
Here are the books I find interesting:
The Elements of Statistical Learning:
This book is great introduction to statistical machine learning. It is contains examples of decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others.
Deep Learning Book:
This is probably one of most read books in Deep learning.
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.
It covers topics from classical Machine Learning to advance topics of CNN and RNN.
Reinforcement Learning: An Introduction
Many people consider Richard Sutton as a father of RL. This book has been referenced over 1000s of times.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field’s intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
There are many courses, but I plan to focus on just a few.
AI Specialization — Andrew Ng
Probably everyone heard of Coursera and Andrew Ng. He is one of the first person who started teaching AI online.
He is now creating the whole new specialization: https://www.deeplearning.ai
DeepLearning.AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community.
Most of their courses are also on Coursera where people can audit for free.
Reinforcement Learning Specialization
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI). One can go through it in less than 2 months.
The course is designed by University of Alberta and main instructors were Prof. Sutton students.
The course actually covers most of the concepts of the book, Reinforcement Learning: the introduction.
Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end.
By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science.
The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more.
MILA Deep Learning School
MILA or Quebec AI Institute is a research institute in Montreal, Quebec, focusing mainly on machine learning research. It is founded by Yoshua Bengio. It is one of the well known research labs in Deep learning.
They offer many sets of Online and in person classes such as: IVADO/MILA DEEP LEARNING SCHOOL: A CHANCE TO STAY AT THE FOREFRONT OF TECHNOLOGICAL DEVELOPMENT .
They also offer Master degree in Machine Learning. For more information check their training page.
Before I wrap this up, I want to introduce some programs for Startups in AI.
NextAI, Montreal and Toronto
If you are based in Montreal or Toronto, Canada, you can apply to NextAI.
They offer a great mentorship and some funding for AI ventures. My team and AI participated in the 2021 cohort and we loved it.
Here is what they offer:
- world class curriculum
- investment to scale your business
- top AI scientists and corporate mentors(MILA and Vector Institute)
- in-kind products and services
Professional Development Certificate in Data Science and Machine Learning
Back when I was a graduate student at McGill, I worked as Head TA for this program. The program is offered by McGill, and it is a great certificate to et part time.
Here is the program description
If you are seeking to acquire essential technical data science and machine learning knowledge and skills, then this program is perfect for you. It will put you on the right path towards a career as a: data analyst, data engineer, data journalist, machine learning practitioner, or data scientist.
Offered online or in-class, you can develop key competencies in data science, including:
1. Application of statistical analysis
2. Machine learning
3. Data mining
4. Data management
5. Data visualization
During the program, you will have the opportunity to work in cross-functional teams to translate your learnings into business insights to help guide business decisions.
This program should be completed within 2 years.
To access the program check this link
All the courses and topics here are introduction and first steps to Data science, I do have a more graduate level courses and books which I am thinking to actually start summarizing, if anyone is interested in them I can put my notes and summary online.
Let me know what you think, and if you want me to share my graduate level notes.
This post was originally published by Farhad Rahbarnia at Medium [AI]