Python machine learning is widely considered the easiest way to start using machine learning for beginners. But why would you want to start studying machine learning techniques?
Once confined to the tech hubs of Silicon Valley, machine learning experts and data scientists are now in demand across the US.
Europe, Russia, China, India, and Japan are also seeing a spike in jobs requiring machine learning skill, as described in this recent article on Forbes (link).
It’s a trend that shows no sign of stopping, so it’s time to skill up and get on board!
What’s that image? This week’s image represents the industrial revolution. According to Wikipedia – The Industrial Revolution was the transition to new manufacturing processes in the period from about 1760 to sometime between 1820 and 1840. We are now in the technological revolution. Where will we be in the next 10 years?
How to Start Machine Learning for Beginners
There are plenty of impressive degrees and PhDs out there to help you become the ultimate machine learning master. But they can be expensive and time-consuming.
If you’re anything like me and just can’t wait to get started applying your knowledge in the real world, an online course may suit you better.
So How Do You Choose an Online Course on Python Machine Learning?
It’s worth pointing out at this point that there are some prerequisites before you can start studying.
Even the courses designed on machine learning for beginners will require you to have some programming skills.
Trust me, I tried a course before I had the essential skills and it did not go well. I could do the theory, but the practical examples were beyond me.
It was incredibly frustrating.
Need to learn some code before you start machine learning? Fear not – you are where I was a couple of months ago.
Check out my post on choosing a programming course here. You can also sign up for my free learning to code cheat sheet with all my tricks to help you skill up fast!
What You Need to Know to Choose a Python Machine Learning Course
Like I said previously, there are plenty of machine learning courses to choose from and why wouldn’t there be? Python machine learning is what all the cool kids are doing.
With so many options it’s easy to get course paralysis.
To combat this in this post, I’m going to outline the pros and cons of three of the best to get you started quickly.
These have all been recommended to me by friends and colleagues working in machine learning now. I’ve also included details of my personal experiences as a beginner taking courses on each of these platforms.
Coursera Machine Learning by Andrew Ng
Andrew Ng is a bit of a super-star in the machine learning space.
His Coursera machine learning course is the go-to place to start demystifying the world of machine learning.
Many practitioners will use the Coursera machine learning materials to refresh their knowledge before interviews and when working with less familiar algorithms.
It was the first machine learning course I tried, and it was recommended to me by a colleague with a Ph.D. in machine learning.
Here’s a YouTube clip of the intro to see the man, Andrew Ng, in action
Before you get too excited though thinking you’ve found the one – let’s break it down.
- User Rating: 4.9 Stars
- Length: 113 videos (between 6 and 20 mins each) + 85 reading resources
- Number of practicals/assessments: 18
- Cost: Free
- You will get a foundational understanding of what machine learning is
- Algorithms explained well – this was something I really liked. If you want to dig into the maths, you can do, and explanations are provided
- Plenty of examples to practice your skills and solidify your learning
- Cost – it’s free!
- Not in python – this is a big one if you are looking to do python machine learning. The programs used, MatLab and Octave, are not very applicable outside of the course.
- Linked to the above, but you will need to do two courses to upskill on python machine learning
- Access – the course is only available at certain times of the year for enrollment – but they are relatively frequent
Though the Coursera machine learning course is not in python, I recently found a Reddit thread on Andrew Ng’s fast ai course which is in python – check out the thread here: link
Course link: HERE
Next, let’s move on to the Udacity Machine Learning Engineer.
Udacity Machine Learning Engineer
Udacity makes excellent in-depth courses with high-quality materials and a focus on practical skills, which is excellent for learning.
I used the Udacity Intro to Programming Nanodegree to learn python. You can read my review here – link.
But this high-quality content comes at a cost – literally!
User Rating: 4.7 Star
Length: 6 months of content – up to 10 hr/week
Number of Practicals: 7 projects – each module is a project
Cost: £799/term – 2 terms total
Let’s check the pros and cons.
- Heavy focus on practical – this will get you applying the techniques from the start
- Covers all the fundamental topics to get you started with machine learning
- Resources and mentors – the support you get on a Udacity course is significantly better than that of other platforms. I used my mentor all the time during my nanodegree
- Hiring partners – Udacity have an excellent Alumni program and an impressive selection of hiring partners to help you get to your next career move
- It’s in python
- Cost – £1498 is a significant cost that can be prohibitive to many people
- Access – enrollment is only available at certain times of the year
- Teach you to do a task – this is not a criticism specific to the ML Engineer course, but Udacity has been known to teach students to do a job rather than focuS on understanding the core concepts
Course link: HERE
Finally, I’m going to talk about the Udemy machine learning course I am currently doing.
Udemy A-Z™: Hands-On Python & R In Data Science
This is the course that my friend recommended to me when I told her I wanted to start python machine learning.
She had just finished her masters in data science and previously completed the course when she was looking to skill up.
I trust her, and so far I have not been disappointed.
- User Rating: 4.5 Star
- Length: 41 hrs of video; 2
- Number of practicals: 49 datasets/exercises
- Cost: Depends – officially £199.99 but as with any Udemy course there are plenty of discount opportunities, I paid £10.99
- Breadth materials – covers all the basics of machine learning
- Suitable for beginners – includes setting up environments, downloading packages, etc
- It’s in python (and in R – two for the price of one!)
- Lots of practicals to do
- Easy to access – no enrollment times
- Mathematical explanation is limited. You may want to pair with the Andrew Ng Coursera machine learning to go deep on these topics
- The depth of content – linked to the above, many of the practicals are there for you to follow along in time rather than work independently
- Pace – this is based on my friend’s feedback that she found the course quite slow
Course link: HERE
Conclusion – So That’s Machine Learning for Beginners
Whichever course you choose it’s great that you’ve decided to learn this skill!
I’m not going to tell you which to go for as it really depends on what you want to get out of it.
I will leave you with these final thoughts, however, to get you to think about your end game, each course has a specific benefit over the others:
- Andrew Ng Coursera machine learning – best for mathematical explanations
- Udacity Machine Learning Engineer – best for support
- Udemy A-Z™: Hands-On Python & R In Data Science – best to get started python machine learning
So What’s Next?
I’ve only just started with the Udemy A-Z™: Hands-On Python & R In Data Science course but am excited to keep learning. First up regression algorithms! Read more here
Want to track progress together? Sign up to my email list to get in touch and stay updated.
Advertising Disclosure: I an affiliate of Udemy and may be compensated in exchange for clicking on the links posted on this website. I only advertise for course I have found valuable and think will help you too.
If you have found this content helpful, I recommend the course linked below which gave me a baseline understanding of the materials and python code shared here.