Machine Learning Engineer

Machine Learning Engineer Nanodegree course by Google

https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009

Have any of you taken this course or are currently taking it?

I'd like to gather some thoughts on it and see the general opinion on the quality of the program.

As far as I know all the components of the course are free but if you want a certificate then you must pay. So no money required.

I'd like to know if it's worth my and other people's time.

Anyone done the Udacity Machine Learning nanodegree program?

Here's the link

It looks really cool but it's also very time-consuming and expensive relative to most online learning.

Still a pittance compared to a university degree however.

Anyone tried it/trying it now?

Udacity Machine Learning Engineer Nanodegree

Anyone Do or Doing Udacity's Machine Learning "Nanodegree"?

I'm looking to get into machine learning, implementing it (currently a full stack javascript developer) & hopefully build a career in it. Don't know much besides the actual science behind machine learning – all I know is that machine learning sounds cool as heck. I have a degree in math, biology (neuroscience courses taken), and economics, & starting Georgia Tech's online Master's in Computer Science this Spring 2016.

From: https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009

The program says that program graduates will be able to:

Analyze the class and complexity of a given problem, and identify an appropriate algorithm and/or tools to apply towards solving it (e.g. gesture recognition, robot control).

Design an intelligent system that can act on the basis of input data towards optimizing some desired goal metric, with minimal supervision from a human.

Analyze the performance of an intelligent algorithm / system and present key metrics (such as accuracy, recall, computing time, etc. as appropriate) in an easy-to-understand and visually appealing form Handle the entire machine learning pipeline, from data to system:

Gather, clean, and process large data sets to prepare them for analysis

Develop multiple models to describe the data in those sets, validate those models, and compare those models according to standard metrics

Convert the data model into a live system that can process and reach conclusions on real data

Optimize the system based on real-world constraints, such as desired accuracy, efficiency, resource availability, and real-time responsiveness

Deploy the system in a live environment, such as an autonomous car, a recommender system, or a personal assistant.

Udacity says that it will take about 450–500 hours to complete the program so it sounds pretty immersive. I think the program is relatively new so I'm guessing not many people have completed it.

Are there any other programs that might be worth looking into? I have trouble learning on my own without some guidance, and generally I find textbooks hard to read if I don't know what I'm doing.

Is this nanodegree in ML any good and worth the $200/month for 10 months?

Finished with coursera ML course, whats next?

I have just finished with the ML course on coursera by Prof Ng, was thinking about the follow-up series also available at coursera by Prof Ng for deep - learning specialization: https://www.coursera.org/specializations/deep-learning

I have also come across this free google course at udacity: https://www.udacity.com/course/deep-learning--ud730

and these nano-degrees as well at udicaty:

Machine Learning Engineer Nanodegree https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009

Artificial Intelligence Engineer https://www.udacity.com/ai

DEEP LEARNING NANODEGREE https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

Did someone here had any experience with these ? are there other better courses\speicalzation that you recommend of?

Thanks(:

Too many courses, confusing terminology! Where to begin with NLP?!

Problem

I want to learn Machine Learning, specifically NLP (Natural Language Processing) for a news analysis project I am working on.

For a person with intermediate programming knowledge and basic knowledge of working with databases, what would be the correct beginning point? There are so many courses available online on different platforms that it's confusing to identify where I should begin.

Existing Skill

I learned programming through the Python specialization on Coursera which taught me about data structures, extracting data from the web, analyzing it and visualizing it. The course established a pretty strong programming foundation but left much to desire when it came to analysis... There was little to nothing about statistics, and from what I've come to know till now Machine Learning requires one to have solid basics in Statistics.

To give you a more granular idea of my current skill level, here's the paper I wrote for my capstone project: https://paper.dropbox.com/doc/News-Analysis-Methodology-fXyowV7zSRAxKA70kxAwP

Options

I am currently looking at Udacity to further my skill but I am getting confused by their different courses on Data Science, Machine Learning, Deep Learning and Artificial Intelligence. There appears to be so much overlap in these courses that it's hard for me to decide what exactly I need.

I don't want to waste time going down the wrong path.

Udacity

https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009

Udacity Nanodegree programs?

There are two nanodegree programs related to Data. Data Analyst Nanodegree program(https://www.udacity.com/course/data-analyst-nanodegree--nd002) and Machine Learning engineer Nanodegree program(https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009). My question is, does Machine learning program covers the same material as in Data Analyst program and also goes more advance?

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

Best online data science/analytics non-degree program?

Looking for recommendations. I am a Data Analyst II proficient in Tableau and SQL. Looking to strengthen and expand my existing skills set. In my opinions these seem to be the best programs offered right now....thoughts?

Udacity: https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009 https://www.udacity.com/course/data-analyst-nanodegree--nd002

FutureLearn: https://www.futurelearn.com/programs/big-data-analytics

If you're ready to make a commitment rather than just purchasing a 25 pound book that's going to gather dust on your book case. And you're sure you want to go for machine learning rather than just farting around and fucking around with bits and bobs, then pay some money for people to train you, there are hundreds of courses out there.

The big brick of a book is just going to burn you out because when you get to page 15 you won't feel like you're getting traction on the material. Getting into machine learning is like getting in peak physical shape, you need a personal trainer to yell at you when you're not getting up early in the morning, setting big goals and then meeting them.

Browse through these links, and find a paid course, one where you pay something like $300 to $1000 from a respected school, and take just once class. Make sure it's graded, with lectures, projects, assignments and a final exam. If you do this, you'll learn to use the material in a real setting rather than just exposing yourself to it.

Andrew NG's coursera course on machine learning: https://www.coursera.org/specializations/machine-learning

Stanford's Andrej Karpathy course cs231n Computer Vision with Convolutional neural nets: http://vision.stanford.edu/teaching/cs231n/ Github: https://github.com/cs231n/cs231n.github.io Youtube link to lectures: https://www.youtube.com/watch?v=i94OvYb6noo

Many different courses under Udacity's nanodegree: https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009 Like: Tucker Balch's course on machine learning: https://www.udacity.com/course/machine-learning-for-trading--ud501

MIT Open Course Ware Machine Learning: https://www.youtube.com/watch?v=_PwhiWxHK8o

FreeCodeCamp and Datacamp has some really good content (focus on Python, stay away from the landfill fire that is R): https://www.datacamp.com/courses/intro-to-python-for-data-science https://medium.freecodecamp.org/ Like for example: https://medium.freecodecamp.org/recognizing-traffic-lights-with-deep-learning-23dae23287cc

Stanford Machine Learning: https://see.stanford.edu/Course/CS229

http://www.themtank.org/a-year-in-computer-vision

If it's that exciting and you're ready to commit to it, then consider going for post secondary education. A college degree in Computer Science with focus on machine learning, then a masters degree in computer science with a focus on machine learning. It'll set you back a lot of money, but it's an investment in time and money, and in theory should return ten times as much.