Machine learning is without a doubt the most interesting field I have come across so far. However, I found it extremely difficult to get started with the basic concepts of machine learning without spending hours and hours scrolling through world wide web. Coming from a web development background, I found it hard to get started with statistics, probability and other concepts related with machine learning. In this post, I will try and list resources that helped me and might help someone else in similar situation.
Andrew Ng’s coursera course - coursera
If you haven’t heard of this course yet, just go ahead and take it.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
Andrew Ng’s stanford course CS 229 - youtube
This is a much detailed version than the coursera course.
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.
Machine learning course CS 156 by Yaser Abu-Mostafa - youtube
This course helps clear many basic concepts. Also, I liked the presentation by Yaser Abu-Mostafa!
This is an introductory course by Caltech Professor Yaser Abu-Mostafa on machine learning that covers the basic theory, algorithms, and applications.
Probabilistic graphical models - coursera
A bit advanced course, but worth taking.
In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.
In-depth introduction to machine learning - r-bloggers
Awesome introduction, This lecture series is provided stanford professors Dr. Hastie and Dr. Tibshirani.
Mathematical Biostatistics Boot Camp 1 - coursera
This class helps refresh basics of mathematics.
Topics include probability, random variables, distributions, expectations, variances, independence, conditional probabilities, likelihood and some basic inferences based on confidence intervals.
Machine learning mastery
A compilation of steps to get started, extremely helpful for beginners
kaggle, The Home of Data Science
Only name is sufficient - The Home of Data Science
Hackernews for data scientists
UCI machine learning repository
Machine learning needs data, UCI give you data.
Even though language is more of a personal choice, machine learning algorithms are hard to implement in every language out there. Thus, some languages are favoured by ML community. Primary languages used as per kaggle, and ML mastery are R, matlab and python. I personally prefer python, because it nicely fits in web stack. But R and matlab are surely great tools as well.
For python lovers, SciPy is a place to go. In case you’re new to python, I recommend learning the language first.
For learning R, there are loads of videos on YouTube. As a beginner in R, it is hard to find one that works for you. I found R Programming Tutorials by MarinStatsLectures quite helpful.
Thank you for reading and I hope this has been an informative post.