Machine Learning A-Z™
Hands-On Python & R In Data Science

This course is a great place to start off for data science and is also really inexpensive 5$ for lifetime access. Just go through the python part and skip the r part of the course.

https://www.udemy.com/course/machinelearning/

este

J'avais commencé ce cours Udemy il y a quelques temps, il était très bien.
Je l'ai jamais fini car j'ai changé de boîte et donc perdu mon accès Udemy pro qui allait avec le taf, mais voila, j'en ai un très bon souvenir.

Thanks for the info friend. Is this the course you are talking about?

I would suggest this course

https://www.udemy.com/course/machinelearning/

I took a time a go and it's a great start.

Cool, a certification will definitely help you. You can try online resources in the mean time. Udemy, Udacity, Coursera, LinkedIn Learning, Linux Academy, ai.google are pretty good resources that you can use and learn from in the meantime.

I am a cloud and big data engineer and have used Udemy a lot. It is fairly cheap and has a "sale" almost every few days. A ML Engineer at my company recommended me Machine Learning A-Z™: Hands-On Python & R In Data Science course. I enver got around to finish this course and it's not exactly a certification but pretty dope for learning.

The team that created Machine Learning A - Z. Its a popular course and I see it in a lot of websites recommended for starting in Machine Learning

If you can afford a paid course, I suggest you take this course from udemy https://www.udemy.com/course/machinelearning/ It's well organized and explained.

I really liked Machine Learning A-Z: Hands-On Python & R In Data Science.

There’s a lot of courses out there, but I really like this one because you get practical examples you can use immediately in the real world.

You won’t be a machine learning expert after completing it, but you will understand the fundamentals and you will be able to create models.

After you finish this, you could start creating models at your work, or you could take more in-depth courses. Andrew Ng machine learning courses on Coursera is often recommended and is much more in-depth.

https://www.udemy.com/course/machinelearning/ It has a bunch of different machine-learning methods, not just Deep Learning. The same company also has a Deep Learning course, but I haven't taken that one yet.

Hey! I highly recommend the udemy course Machine Learning a-z. https://www.udemy.com/course/machinelearning/

Im doing this one right now https://www.udemy.com/course/machinelearning/ .. it’s often 90% off.. wait till the discount and you could get it for 12$ im learning so much and it’s to the point and really good

Ah....I assumed you meant "uploaded the full [pirated lecture portion of the] course" like this udemy machine learning course.

https://www.udemy.com/course/machinelearning/

I mean you COULD complete the course without doing any work...but what would be the point of getting credentials without the experience.

Conversely I could download tensorflow and help with some random handwriting recognition project, which produces the experience without credentials.

So, let me rephrase the question: if you were browsing machine learning jobs, what would you cite as your professional credentials?

Here is the course I’m going through and it is amazing. It teaches you regression and classification algorithms, clustering algorithms, association rules, reinforcement learning, neural networks, and goes into the theory and intuition into each concept and how to implement them in Python and R. It is definitely worth the price. After completing this course, you will definitely have the knowledge to do your own data science and machine learning projects.

https://www.udemy.com/course/machinelearning/

The way I did it was I got on udemy and found a machine learning course. The course showed you how to use like 20 different ML Algorithms in python by importing libraries and using Google Collaboratory which is the same thing as jupyter notebook.

https://www.udemy.com/course/machinelearning/ maybe you can get this for free with wgu idk.

Then go to kaggle and pick a dataset that you recognize one of your machine learning algorithms will work with. Once you have that you can do task 1. then task 2 is just the paper and creating the project.

for example https://www.kaggle.com/sakshigoyal7/credit-card-customers imagine you chose this dataset you could predict salary, age, etc. and it would make sense for a business need. I guess their goal is they want you to predict churned customers whatever that is.

so in summary watch the udemy video get comfortable with a bunch of ml algorithms (which is not hard you don't make the algorithm yourself you just implement it by importing the library.) Then find a dataset with a made up business need. Predicting something! hope this helps you get started.

Hello!

I've been working as a business analyst for the last 3 years and now I'm looking to broaden my knowledge and since I'm not very happy in my current position, maybe look for a job where I can explore more of data science and machine learning. I'm very familiar with SQL, I have some knowledge of python and I know the basic ideas about machine learning. I had bought this course on udemy some time ago and decided this was my starting point in this journey. Here are my questions about this:

1. For those who elarned the same way I'm doing, how much time did you invest in this per week?
2. How do you practice this? I understand the concepts but after doing the course exercises I struggle to know where to go next. Do you look for random databases and try to apply what you have learn or is there a more methodical way to do it? Maybe different courses with exercises?
3. What courses would you recommend me to do next? Some with the same contents to have a different view on this? Different subjects? This course is part of a series on superdatascience but I don't this it's worthy to do them all

Start with loading dataset with pandas, then check logistic regression, K-nearest neighbors, Decision Trees, Random Forest. And as you learn, you will see what you need to explore more.

I started with udemy course (https://www.udemy.com/course/machinelearning/), but beside some basic terminology and concepts, I didn't learn something more. Real-life project are way more complicated than the projects presented there. But first I recommend you to check the suggested youtube channel, it looks good.

I can tell you about Ng's ML course. I completed it last year. It is very Mathematical. All the important ML algorithms are explained in great detail with their mathematical intuition. Linear regression, logistic regression, Neural networks, Support Vector Machines, Dimensionality Reduction, Anamoly Detection, and Recommender Systems are the major topics that are covered. Along with this, Ng shares his knowledge on the nuanced topics like Regularization, Gradient Decent, Pipelining and some general advices along the way.

The only drawback of the course is that you won't be applying these algorithms to real world datasets. All you will be doing is coding out the algorithms in OCTAVE or MATLAB which I think is pretty much outdated. Python and R are widely used for Machine Learning now. When you will start participating in ML competitions on Kaggle etc., you will have no idea what to do. You don't need to write down the algorithms, you'll need to simply import the module. You will end up taking another course. The course is also not updated much after it's release, it is kind of a classical course.

If knowing the Math behind the above mentioned algorithms is your aim then go for it (Probability models and Tree Models are not included in the course).

If you are more inclined towards learning the practical implementation, I suggest you enroll to Krill Eremenko's Udemy course Machine Learning A-Z™: Hands-On Python & R In Data Science'. He is a great tutor and his teaching is very early to follow. Also, if you want to understand how algorithms actually work, you MUST subscribe to StatQuest on YouTube. He is the best out there. I hope this helps.

https://www.udemy.com/course/machinelearning/

This was my initial foray into Python and Data Science. I found it to be really good (at least as good as any other course) at providing common sense direction into how to use machine learning and gave a really good tutorial on structuring code and building out the script needed to complete tasks.

start this course(python, fuck R) - if you enjoy it - reply to this comment and I'll give you some further things to look at.

https://www.udemy.com/course/machinelearning/

>here for a progressive guide through with lots of advice

I think he is talking about this one. https://www.udemy.com/course/machinelearning/

As a complete beginner I thought I'd add my thoughts here of my experience thus far:

Purchased a ML course on Udemy due to a suggestion from a user over at r/datascience (my post was subsequently deleted by a mod as I suspect I broke a rule by posting outside of the weekly sticky)

This is the suggested course: https://www.udemy.com/course/machinelearning/

I am personally experiencing a few problems though. Even though the course is geared towards beginners to ML, it doesn't take you through the basics and/or core principles of Python (or R depending on which platform you choose as the course covers both). Not sure about your learning preferences, but for me to understand a process I need to know WHY something has taken place, not simply remember a string of commands to just use them in the future. Whilst I 'passed' the first course module (Data Preprocessing) by simply copying the instructor's code, I left the end of that module with very low genuine understanding of how we got there.

So if the instructor mentions a specific library (I didn't know what a library was a week ago), and a subsequent module, whilst he will generally explain the WHY with a top-level explanation, I then have burning questions about the heirachy of the library>module>functions & classes and how they relate to one another, then pondering if I need to study them independently or only delve into them only when required?

Other challenges I've had relate around where why and when parenthesis '( )' are required and why in some instances they can be empty, but still need to be there? and what is the difference between square parenthesis '[ ]' and the standard ones we see in day-to-day text communications '( )'

I have more questions, none of which I have answers to as yet (and I'm not asking the folks here - simply outlining my own state of mind at present)

As a result, I've resigned myself to take a step back and try to find a learning tool that gives me a solid background on the fundamentals, with the hope it will help accelerate the ML learning so I'm not constantly side tracking for 1-2 hours at a time across to Google in order to understand a basic principle that the course is going through.

In literacy terms, I feel like I've signed myself up to a course on short story writing before learning how to write "dog" "cat" and "apple"

Just something to consider if you are indeed a complete beginner as I am.

EDIT: Added the course module

I found "Udemy's Machine Learning A-Z™: Hands-On Python & R In Data Science" to be great. Link below:

https://www.udemy.com/course/machinelearning/

Python libraries:

• For handling data and numbers: numpy, pandas
• For plotting: matplotlib
• For modeling: scikit-learn

Most introductory data science/ML classes will teach you the relevant Python or R libraries/packages, so I don't think you have to try too hard to actively look it up on your own (e.g. see the offerings from udemy, edx, coursera -- not endorsements/I've never taken any of these, just ones I found by Googling) .

Have you tried any Udemy courses? Don't expect masters level, but it's good for getting the hang of Data science techniques.

https://www.udemy.com/course/machinelearning/

https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/

Which course should I choose?

Hi guys, first time posting and the english is not my mother tounge, so sorry in case of any mistake. I really wanto to get into ML, and I'd like to purchase a Udemy course taking advantage of the discounts, however I'm between two possiblities and I don't know which one could be better for me, the courses are:

Machine Learning A-Z™: Hands-On Python & R In Data Science (https://www.udemy.com/course/machinelearning/)

Python for Data Science and Machine Learning Bootcamp (https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/)

I'd really appreciate any advice or help

There is a Udemy course called Machine Learning A-Z™: Hands-On Python & R In Data Science [+] which I personally began with.

There is also Machine Learning Guide [+] podcast which I found surprisingly useful, both in explaining terms, concepts and roadmaps, as well as introducing learning resources.

There is the renowned Machine Learning by Andrew Ng [+] which is some sort of celebrity among similar courses!

And as for books, I'd recommend Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow [+] by Aurélien Géron.

Try not to stress too much, just absorb everything you can in your internship. There are a few discount courses online to help you learn skills. I wouldn't expect to know everything there is to know from such courses but maybe you'll have some use with them. I'll link them below.

Just follow your mentors guidance. Study material, get that presentation ready and continue on being an active learner.

Machines Learning A-Z: Hands-On Python & R In Data Science

Deep Learning A-Z: Hands-On Artificial Neural Networks

Machine Learning, Data Science & Deep Learning with Python

Hmm, it seems like you are asking you want to start with ML and already have the math background. The mml book is really nice and I have seen many people using it and also on slack channels.

Generally, everyone starts with Andrew Ng's Machine Learning course on Coursera. It's comprehensive and goes into the math. There's also a youtube playlist.

I personally did the Machine Learning A-Z course on Udemy before doing Andrew Ng's course. This Udemy course does provide you with many coding exercises and examples and makes use of the sklearn library a lot. However, Andrew Ng's course makes you implement models from scratch in numpy.

Finally, Kaggle. Kaggle also has some courses categorized by topics. Although the courses are based around coding and applications to problems more than the workings of the algorithms themselves.

My role is an ML Engineer, however I have responsibilities of taking decisions regarding the solution I wanna implement, etc. I studied Computer Science under KTU so I started out the natural route of programming into data science. I had projects in Data Science which helped me land this role.

Now coming to your question, what qualifies someone as a data scientist. They are basically someone that solves a problem in the industry using data. They use data science as a tool to solve a particular problem. So to land a job, you need to prove that you can solve problems. First and foremost, if you only have academic experience, start by learning what is used in the industry. And more importantly when to use it. Build a good intuition about that. This will enable you to solve problems in kaggle, hackerearth, etc and build a good portfolio. And also help you develop your coding and engineering skills. Additionally go through tech blogs of how companies are using data science to solve their problems. This will help you frame solutions.

And finally, you need to 'market' yourself in the industry. Do this by creating a good portfolio on github, kaggle, etc. Have a good linkedin profile. Write blogs, reach out to recruiters, etc. Landing a great job without prior experience is very difficult. Refer to sites like angellist and linkedin to find startups and start messaging people there for an internship or an entry level role. Once you are established with sufficient experience, you will be able to find good opportunities if the current demand holds.

Posting some links which helped me in my journey:

Machine Learning by prof. Andrew NG

Machine Learning A-Z course on Udemy - A very good course if you are just a beginner and want to build a very basic understanding of different algorithms.

Machine Learning Plus - Has some great blogs around time series analysis, programming, etc. Also has some paid courses which focus on a detailed walk through about solving real world problems.

Hi, I recommend getting some course from Coursera or from Udemy. I attended two courses on Udemy that explain the basic concepts of AI and give you a good background to start developing your skills by putting your hands dirty by coding.

links here: https://www.udemy.com/course/machinelearning/learn/lecture/6087180#overview
https://www.udemy.com/course/deeplearning/learn/lecture/6820150#overview

Thanks! There are nowadays lots of good courses/tutorials to get you started and I'm gonna provide few courses/books here that I found useful in my ML journey:

• http://faculty.marshall.usc.edu/gareth-james/ISL/ Excellent book to get you started. This books contains moderate amount of math but I found this one still easy to grasp. Book provides also nice R code snippet to test models on different datasets
• https://www.udemy.com/course/machinelearning/ This is a great (but lengthy) course to get you started in Machine Learning. This basically skips most of the math and goes straight into hands on learning with Python and R provided for this course. In my opinion this a good starting point

Those two were deal breakers for me that helped me to get into Machine Learning. Remember that learning ML is not a sprint it is a marathon

If you're looking for online courses you can try the udemy ones.. I went through these when i was a beginner. https://www.udemy.com/course/machinelearning/ and https://www.udemy.com/course/deeplearning ). Then I did the Google Tensorflow course on coursera and the linked kaggle NYC taxi fare prediction challenge. The udemy one covers a wide range of topics from statistics, machine learning and AI but its basic, for desktop modelling, not production scale. The tensorflow course is more narrow on just linear, non-linear models and fully connected neural networks , but prepares you for production scale coding.

My favorite thing about computer science is that the kinds of things that you do as a professional are basically the same as the things you do as a beginner. They're just more complex and build on those basics. So if you want to find out if you would like coding and want to dedicate yourself to it more, the best thing to do is to find any kind of basic training course in the specific thing you're interested in, follow it all the way through, and see if you like it. Then, try to make something in that area. As you're trying to make your own thing that you're interested in, there will be questions that you don't know the answer to, and you can take additional courses to answer those questions.

My favorite place to learn the specific answers to questions is stack overflow and the documentation for the programming language I'm writing in. They'll have the most detail.

But my favorite place to learn a new skill in software is Udemy. The courses are like $12 each, and if you pick one that's popular, they're usually really good. They're almost all project based, so instead of spending hours trying to teach you all the basics before you put it together, they walk you through a project from beginning to end, so you learn how to structure a project overall and how the pieces fit together, and you have context not just for how to write a Singleton, for example, but for why you'd want to use a Singleton.

Since you mention wanting to get into data analysis, if you are already confident in math in general, I'd highly recommend this Machine Learning course https://www.udemy.com/course/machinelearning/

If you want more a friendly approach to see if you like writing software, I'd recommend either this course for Web Development: https://www.udemy.com/course/the-complete-web-developer-course-2/

Or this course if you're more interested in making video games: https://www.udemy.com/course/unitycourse/

Both are very project based, interesting, and easy to understand and apply what you've learned to your own projects.

​

But super duper important, if you're going to use Udemy, they go on sale all the goddamn time. All the courses say "This is $100". They don't cost $100. Wait for a sale or find a coupon code, you'll get it for under $20.

Few free data science courses on Udemy for 3 days (Offer ending today)

1. Data Science A-Z™: Real-Life Data Science Exercises Included
2. R Programming A-Z™: R For Data Science With Real Exercises!
3. Python A-Z™: Python For Data Science With Real Exercises!
4. Tableau 10 A-Z: Hands-On Tableau Training For Data Science!
5. Power BI A-Z: Hands-On Power BI Training For Data Science!
6. Python for Statistical Analysis
7. Machine Learning A-Z™: Hands-On Python & R In Data Science

Reference: https://sdsclub.com/lockdown-giveaway/

Sure.

Machine learning / Data science is such a huge field and sadly I don't have a optimal way to break into it. Usually I needed like 2-3 courses on each topic before I understood it.

I think this one is a good starter:

https://www.udemy.com/course/machinelearning/

It has its problems.

- You almost just watch half of it, because you are only watching the python stuff ,not the R.

-It is a lot of just typing what they are typing.

I have taken a course from Lazy programmer in reinforcement learning, that I liked. It was the complete opposite of the course above. Few coding instructions and alot of thinking for your self. He got courses on introduction to machine learning, which i haven't taken though.

https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/

The first course I took was Andrew Ngs machine learning course.

https://www.coursera.org/learn/machine-learning

It is very math-heavy. You code in Matlab/octave. I liked it but it don't think it is for everyone.

I am not sure if my time invested in machine learning / Data science was worth it, since I feel that I would have to invest very much more time before anyone would hire me because of my machine learning skills. I did get an offer to do my bachelor thesis on a machine-learning company. I chose to start working instead. I sometimes regret it but it is impossible to know which decision I should have made.

Retrain something in the IT field (Customer Service Support Role, Programming role, Sales role)?

A start could be a free course on the internet to try yourself out and see if you like it. Download VSCode (for Windows) and learn some little Python and see if you like it.

• The Python Open Source Computer Science Degree

• The Open Source Computer Science Degree

• MIT Open Coursware Python late 2016 MIT 6.0001 Introduction to Computer Science and Programming in Python, Fall 2016 Instructor: Dr. Ana Bell
  & https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/

• Python for Everybody - Full Course with Dr. Chuck

• And Udemy has still a Sale on eg Machine Learning/AI w Python or another Python Class here.

Or, Horticulture? RHS said there are not enough Horticulturists.

Several highly rated DS, ML, Python, Tableau, PowerBI video learning courses are free to purchase (only for 1 day), buy them before they are gone tomorrow. pass these to others who may be interested.

https://www.udemy.com/course/datascience/?couponCode=LOCKDOWN_GIVEAWAY

https://www.udemy.com/course/r-programming/?couponCode=LOCKDOWN_GIVEAWAY

https://www.udemy.com/course/python-coding/?couponCode=LOCKDOWN_GIVEAWAY

https://www.udemy.com/course/tableau10/?couponCode=LOCKDOWN_GIVEAWAY

https://www.udemy.com/course/mspowerbi/?couponCode=LOCKDOWN_GIVEAWAY

https://www.udemy.com/course/python-for-statistical-analysis/?couponCode=LOCKDOWN_GIVEAWAY

https://www.udemy.com/course/machinelearning/?couponCode=LOCKDOWN_GIVEAWAY

https://www.udemy.com/course/tensorflow-2/?couponCode=LOCKDOWN_GIVEAWAY1

https://www.udemy.com/course/modern-nlp/?couponCode=LOCKDOWN_GIVEAWAY1

https://www.udemy.com/course/deeplearning/?couponCode=LOCKDOWN_GIVEAWAY1

Python

Learn to Program: The Fundamentals - https://www.coursera.org/learn/learn-to-program

Learn to Program: Creating Quality Code - https://www.coursera.org/learn/program-code

Complete Python Bootcamp: Go from zero to hero in Python 3 - https://www.udemy.com/course/complete-python-bootcamp/

The Python Mega Course: Build 10 Real World Applications - https://www.udemy.com/course/the-python-mega-course/

Machine Learning

Machine Learning - https://www.coursera.org/learn/machine-learning

Applied Data Science with Python Specialization - https://www.coursera.org/specializations/data-science-python

Machine Learning A-Z™: Hands-On Python & R In Data Science - https://www.udemy.com/course/machinelearning/

Deep Learning A-Z™: Hands-On Artificial Neural Networks - https://www.udemy.com/course/deeplearning/