Introduction to Machine Learning Course

There's a free Udacity course, Intro to a Machine Learning, using Python if that's what interests you. https://www.udacity.com/course/intro-to-machine-learning--ud120

What are some of the most fun online programming tutorials/courses that you have stumbled upon?

I know "fun" is an abstract term but anything that you think was interesting and immediately got you hooked as opposed to being strictly tutorial-ish fits. Maybe some course had a really great, laid-back instructor, or used gamification in a non-obvious manner, or maybe it just proved to you that some cool project could be completed in one weekend even though you would have never suspected so. Any language, any difficulty level - I just think such a list is something everyone would benefit from.

As for me, I guess I'd recommend:

• Udacity's CS101 for the very beginners cause it immediately got me hooked in Python back when I took it by showing that even creating very complex-sounding things like a search engine is possible over such a short timespan
• Udacity's Intro to Machine Learning for the more advanced ones. I (and I guess - many people) always viewed machine learning as a fascinating but very complex and inaccessible field which demands lots of theoretical knowledge to even start playing with - and this course proves you completely wrong in that aspect by showing you a series of mini-projects that you can immediately get you feet wet in

Intro to Machine Learning Course from Udacity

Artificial neural networks are great, but keep in mind that they're just a means to an end. The best way to learn them is to go through a good textbook or online course where you'll try them out on good examples that have been designed specifically to be good for beginners.

To a professional, you don't start with the tool and search for a problem - you start with a problem and figure out the best tool. Sometimes that tool is neural networks, but probably 99% of the time it's not. Even when the right tool is "machine learning", there are a lot of machine learning techniques other than ANNs.

As a beginner, the best thing you can do is start by learning about machine learning in general. You can't properly use ANNs if you don't understand the principles of machine learning in general, which is what the book or course I linked above will give you.

​

https://www.udacity.com/course/intro-to-machine-learning--ud120 free (if you don't click on the wrong shiny button lol)

Great introduction to all machine learning topics up to neural networks.

Keep in mind that CS isn’t the best background to start with for data science, so don’t let you not being from a CS background discourage you. My best advice is to buy books.

Beginner: Data science from scratch. Python data science handbook.

Intermediate: Introduction to statistical learning. Hands on machine learning with sci-kit and tensorflow. Maybe a time series book if you care about it too. In my experience time series is overlooked a lot and has so many valuable applications in a business.

I only ever found one online course really valuable when starting from scratch and that was https://www.udacity.com/course/intro-to-machine-learning--ud120

If you want to play with what you already know deeplearning4j and spark's mllib are both battle tested.

However they are not my recommended approach.

The argument about being production ready is invalid. Our entire backend stack is solely comprised of Java (more than 1000 microservices) and yet we only use python to train and serve models. And serving is actually pretty decent, we can easily handle over 10,000 requests per second on each model.

First of all all the new libraries come python-first so you have a very big leg up. There's a very big python community around ML now and you definitely want to be in that ecosystem.

Second, the prototyping phase is much faster in python since you dont need to recompile your code all the time and all your data is already in memory thanks to jupyter notebooks.

Third, I dont recommend doing prototyping in python and serving in java because the code implementations of the model can be subtelly different and cause impossible to debug issues.

I've tried most ML courses including Andrew Ng's and I'd say bar-none the best for beginners is Udacity's intro to ML https://www.udacity.com/course/intro-to-machine-learning--ud120

Hope that helps!

Try Udacity's Intro to Machine Learning course. Not the Nanodegree. https://www.udacity.com/course/intro-to-machine-learning--ud120 It will equip you with the basics and you can work on projects. Try fast.ai for Deep Learning. That is seriously awesome! All the best.

I took their free intro to machine learning course: https://www.udacity.com/course/intro-to-machine-learning--ud120

I thought it was really helpful for learning the basics and very hands on. Im not sure I even finished it, but it got me caught up enough that I could move on to more advanced things on my own. It covered mostly sklearn in python. If you are new and want to learn how to implement ML algorithms right away I would definitely recommend it!

Haven't personally tried it, but it's on my list. If you have a go at it, do let me know how it is https://www.udacity.com/course/intro-to-machine-learning--ud120

> I currently have individuals who are able to connect APIs to exchanges and to the algorithm, and will implement the algorithm I create into a software program.

Nobody creates a new machine learning algorithm unless they have a Phd in that field (and even then, rarely). You will probably want to use an existing algorithm, and tweak its parameters to fit your needs.

​

From what I have seen in academic conferences, it is a common practices to just try all machine learning algorithms of the python library, and use the one that performs best. Now, having knowledge on how these algorithms work may help. Here is a free online course that teaches basics.

​

Finally, all the knowledge in the world is useless without good-quality data. You have to have at least an idea of what factors might influence the future price, and have data on those factors (from the past for example).

I found this Udacity course to be helpful. It is a mix of theory and practical exercises.

Help critique my ML curriculum!

Hi everyone! I've been doing some research on how to get started on machine learning as a beginner and have come up with the curriculum below for myself to follow. It would be great if those proficient in ML, or even those currently learning, help to critique or provide suggestions. Additional courses, literature, programs, or advice would be greatly appreciated! It may be difficult to critique without having firsthand experience of each course or program, but I'm more concerned the with subject matter included than the courses themselves. As I go through each course, I may decide to change them if I feel that it is not pertinent to the goal or lacking in quality. There's no way I will become an expert after this, but I'm simply looking for a good starting point. I hope this will help others as well.

 

As a point of reference, I've graduated with a B.S. in Mechanical Engineering, have taken several math courses (calculus, linear algebra, differential equations), and have taken a few programming courses (CS50, Intro to Comp Sci on eDX, CS 101 at University). I'm currently working part-time as a mechanical designer / energy analyst and have 40-50 hours a week to spare.

 

For the curriculum, I've broken it up into 5 sections (programming, statistics, linear algebra, data science, and machine learning) which will be overlapping. I plan on taking 3 courses at a time starting with Udacity's Intro to programming, Stanford's probability and stats, and Udacity's linear algebra refresher while reading "An Introduction to Statistical Learning."

 

Here it is!

 

Curriculum:

Programming

1. Udacity Introduction to programming https://www.udacity.com/course/intro-to-programming-nanodegree--nd000
2. Learn the basics of programming through HTML, CSS, and Python.

Statistics

1. Stanford Online: Probability and Statistics https://lagunita.stanford.edu/courses/course-v1:OLI+ProbStat+Open_Jan2017/about

2. Broken into four sections: exploratory data analysis, producing data, probability, and inference.

3. Stanford Online: Statistical Learning http://online.stanford.edu/course/statistical-learning-winter-2014

4. Introductory-level course in supervised learning, with a focus on regression and classification methods.
5. Lectures cover all the material in An Introduction to Statistical Learning, with Applications in R.

Linear Algebra

1. Udacity Linear Algebra Refresher Course with Python: https://www.udacity.com/course/linear-algebra-refresher-course--ud953

2. UAustinX Linear Algebra - Foundations to Frontiers edx.org/course/linear-algebra

3. Connections between linear transformations, matrices, and systems of linear equations.
4. Partitioned matrices and characteristics of special matrices.
5. Algorithms for matrix computations and solving systems of equations. Vector spaces, subspaces, and characterizations of linear independence. *Orthogonality, linear least-squares, eigenvalues and eigenvectors

Data Science

1. Udacity Introduction to Data Science https://www.udacity.com/course/intro-to-data-science--ud359

2. Focuses on Data Manipulation, Data Analysis with Statistics and Machine Learning, Data Communication with Information Visualization, and Data at Scale -- Working with Big Data

3. Udacity Data Science Nano-degree https://www.udacity.com/course/data-analyst-nanodegree--nd002

4. Learn to organize data, uncover patterns and insights, make predictions using machine learning, and clearly communicate critical findings.

5. (optional) UCSD Data Science Micro-masters Program (4 course program) https://www.edx.org/micromasters/data-science

6. Four courses: Python for Data Science, Statistics and Probability in Data Science using Python, Machine Learning for Data Science, and Big Data Analytics Using Spark.

Machine Learning

1. Udacity Intro to Machine Learning https://www.udacity.com/course/intro-to-machine-learning--ud120

2. How to extract and identify useful features that best represent data, learn a few of the most important machine learning algorithms, and how to evaluate the performance machine learning algorithms.

3. Coursera Machine Learning by Andrew Ng https://www.coursera.org/learn/machine-learning

4. This course provides a broad introduction to machine learning, data-mining, and statistical pattern recognition.

5. (with job guarantee?) Udacity Machine Learning Nano-degree https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009

6. Apply predictive models to massive data sets in fields like finance, healthcare, education, and more.

 

Literature:

1. An Introduction to Statistical Learning http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf

2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf

 

Other (after curriculum):

https://www.reddit.com/r/MachineLearning/wiki/index

 

Let me know your thoughts! Have you taken one of these courses before? Think there's too little programming or know of a better course/program? Chime in! Would be really grateful for any feedback.

Big Thanks!

-sleepyowl

https://www.udacity.com/course/intro-to-machine-learning--ud120

https://www.udacity.com/course/intro-to-machine-learning--ud120 Here is link of free udacity's intro to ML course

You can start with Udacity's Intro to Machine Learning (free), or Coursera's Machine Learning (free) if you don't mind doing exercises in MATLAB/Octave.

https://www.udacity.com/course/intro-to-machine-learning--ud120 This will give you an idea of what's possible and the tools you might end up using. Definitely look at the model capstone archive. From there, Kaggle has tons of data sets and ideas that you can either use or adapt into something you can use for capstone.

ok just read.

https://www.arduino.cc/en/Tutorial/HomePage?from=Main.Tutorials

https://github.com/ethereum/wiki/wiki/White-Paper

https://www.udacity.com/course/intro-to-machine-learning--ud120

Robotics, FinTech, Machine Learning. Build something cool.

Probably AI -> machine learning -> Deep Learning

Intro to AI

Intro to ML

Intro to DL

They're not necessarily sequential things and sort of build off each other both ways i.e. you use deep learning for advanced AI but you don't want to do DL without knowing ML first which is similar to basic AI etc.

If you are not mathematically inclined, check out https://www.udemy.com/machinelearning/learn/v4/overview

I also suggest this if you want to be an over-achiever : https://www.udacity.com/course/intro-to-machine-learning--ud120

If you find you are really enjoying it or can convince your employer for this, i suggest this https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t

Deep Learning with Python by Francois Chollet, the creator of Keras, and is a good, quick way to get started with neural networks.

There's also a free Udacity Intro to Deep Learning with PyTorch course.

Keras is a wrapper for TensorFlow, which is very popular, but I personally find PyTorch more intuitive and fun to work with.

Neither of those teach you the fundamentals of machine learning though, which IMO is crucial to fully understand deep learning/neural networks. The free Udacity Intro to Machine Learning course will give you good foundation without too much theory or math.

​

At my current work they’re all in house as they work really closely with other team members (PMs), but other companies certainly do remote or contract data positions where they’ll ask a bunch of questions and you prepare a big report or insights and send it back to them or travel in to present.

Things to look at : - Google Big Query - Amazon Kenesis (& Machine Learning) - Tableau

this coursera

this EDX

• Intro to machine learning

• A whole masters course, I’ve never done it but it looks interesting and free

I also switched careers into DS. The first course I took was https://www.udacity.com/course/intro-to-machine-learning--ud120 It is taught by Sebastian Thrun and Katie Malone and it is free. I can't recommend this enough. They try to 'hold your hand' as you walk through the code and do a great job explaining the fundamentals.

​

If you have absolutely no coding experience (like never wrote a line of python) I would do a short tutorial on that first. The one I did before I took the course was https://www.practicepython.org/ just do a few to get a handle of the basics

Hey, i'm not an expert but i do learn a lot and have some cool resources for you.
Frameworks:
- sklearn this is a good starting point, easy to use if you want to try things or make some basic idea, like a recommender, or dog/cat classification.

- keras/pytorch they provide a nice way to make neural networks and start learning some deep learning.

- tensorflow i think it's the lowest level you can go with frameworks, it's the most powerful of the neural networks frameworks but the hardest to learn, it's cool if you want to have more knowledge of the low levelish things, but if you want to make things and learn more about the higher level stuff stick with keras or pytorch

Some nice courses:

- The ol' andrew ng the classic of classics, and don't like that it uses MatLab but it's one of the best courses out there, and you will learn a lot about theory and math

- Intro to ML (Udacity) this one i think is much more pragmatic, they teach a new concept in short lessons, and it uses sklearn to teach each model, i recommend it if you want to start coding fast

Books:

- Hands-On Machine Learning i think this is one of the best book out there, it teaches the mathematics but without being too overwhelming and it teaches a lot of the practice, first using sklearn and latter deep learning with tensorflow, it's an amazing book!

Youtubers:

- Siraj very funny and brief, a lot of videos of cool things, may be too fast and too hard to code anything from the videos without more knowledge, but it's very good to learn how things works

- 3blue1brown amazing mathematical videos

​

Discrete Math and Algorithms and Data Structures

there are just as many resources for Machine Learning as their are for Algorithms and Data Structures

https://www.udacity.com/course/machine-learning--ud262

https://www.udacity.com/course/intro-to-machine-learning--ud120

https://www.udacity.com/course/machine-learning-for-trading--ud501

^ those are free

these are free, but have a pay option for graded work (also have to pay to have lifetime access)

https://www.edx.org/v2/course/machine-learning

https://www.edx.org/v2/course/machine-learning-fundamentals-3

https://www.edx.org/v2/course/data-science-machine-learning-2

https://www.edx.org/v2/course/principles-of-machine-learning-python-edition-3

so you an learn Machine Learning on your own time (assuming you have already taken Calculus and Linear Algebra -- if you haven't then take them as electives now)

First of all, I could recommend to study Python. Why? There are some reasons.

1.Python is the fastest growing programming language. You can use it for ML applications, data analysis, visualization, web apps, API integrations, etc.

2.It’s one of the easier languages to pick up and learn.

Below some sources that I think will be good for your self learning.
https://learnpythonthehardway.org/

https://community.modeanalytics.com/python/tutorial/pandas-dataframe/

If you haven't learnt ML before, check also these free online-courses
https://www.udacity.com/course/intro-to-machine-learning--ud120

https://www.udacity.com/course/intro-to-statistics--st101

https://www.udacity.com/course/intro-to-data-science--ud359

Hope you'll find it useful!

For self path study, I prefer to watch free lecture videos on youtube / Udacity and do some projects.

If you want to refresh data structure, I recommend you to try UCBerkeley CS61B, one of the famous class there https://sp18.datastructur.es/

It's open for everyone, I have been working on their course work/ projects since last month. Very good explanation on the topics, also allow us submit the homework and projects for autograding.

Algorithms:

UCBerkeley: https://inst.eecs.berkeley.edu/\~cs170/fa18/

Standford: https://www.youtube.com/channel/UCH4s4ek5zqNvct5oy9_jd_g/playlists

https://www.coursera.org/specializations/algorithms

Intro to Machine Learning:

https://www.udacity.com/course/intro-to-machine-learning--ud120#

https://www.coursera.org/learn/machine-learning (beginner to intermediate level)

Intro to AI:

https://www.udacity.com/course/intro-to-artificial-intelligence--cs271

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