Are you one of those self-taught Machine Learning enthusiasts who followed this route –self-learnt Python or R -> Machine Learning MOOCS -> Data Science Bootcamp. The one thing missing in this is the end goal – landing an enviable Data Science position which begs the question – how can beginners who finished Deep Learning and Machine Learning MOOCs take it to the next level. How can one push the learning curve forward?
Firstly, the usual consensus is that ML Introductory courses and Deep Dive courses are top notch and are the number one resource to get started with ML. However, the Deep Learning MOOCs don’t dive into the complex topics and even the exercises require a lot of self-research.
The free Deep Learning course offered by a leading ed-tech has been panned for just giving a broad introduction. To succeed after enrolling in a MOOC, it is imperative to find a course with the right level of depth. Besides a complete understanding, MOOCs will also give you a clear path to study ML and DL for the future.
Here’s where MOOCs have evolved to become real assets. Since Machine Learning and Deep Learning are relatively new fields, it is hard to find top-notch talent. MOOCs present an incredible opportunity to people to retool themselves in machine learning and DL. Plus, the Capstone project at the end of study simulates real-world issues and equips the learners with practical experience. However, MOOCs also come at a steep price and you would want it to justify its value by building aggressively on top of their learning experience and giving a solid ROI at the end.
AIM chalks down ways on how one can take their MOOC learning forward:
Make the most of MOOCs by learning the prerequisite tools: One of the primary reasons MOOCs took off and the companies are seeing a stupendous year-on-year growth in this field is because the curriculum is aligned with in-demand industry skills. Hence, if you are planning to pursue a Nanodegree, then gain a deeper understanding of the major prerequisite tools and frameworks.
Build a job-ready portfolio before you finish your micro-degree: In machine learning, it isn’t just important to learn the fundamentals and grapple with the nitty-gritties of Regression and Classification. You can add more weightage to your CV by building projects around the industry you wish to work in. What works best with recruiters and hiring managers is having a job-ready portfolio of work. Post it publicly on GitHub, LinkedIn or even your personal site– which will add more weightage to your CV than a Master’s program and will add more credential. The best part about the work portfolio is that it acts as a great talking point during job interviews.
Apply theoretical concepts in real life: Most MOOCs often skip some aspects of machine learning, such as how does one prepare the dataset, how does one explore the data and how to turn the model into a data-driven product. Finally, how does one communicate the result. If you want to excel in machine learning, you should push the learning curve forward and become a proficient user of a popular machine learning library such as scikit-learn, shared a data science enthusiast on a forum. Also, put your learning in action by doing end-to-end projects on data analysis, visualization, data wrangling, machine learning with hyper-parameter tuning and evaluating different models.
Find out your application area of interest: The next goal should be to find out whether you lean towards theoretical machine learning or application oriented problems? Is there a specific area of machine learning you would like to focus on, such as Deep Learning, Deep Learning for Speech and Language or graph mining? Do you have a domain in mind where you want to apply your machine learning algorithms such as business development, healthcare or bioinformatics?
Be on a lookout for projects that need your skills: Undoubtedly, Machine Learning and Deep Learning are buzzing fields and bagging these online credentials could be a lifelong asset. With continual learning becoming a norm, nanodegrees have become the stepping stone for fast-tracking your skills. And the reason why MOOCs are so popular is because you pick up targeted skills employers need and the programs are delivered in partnership with leading companies, which gives them a ready pool of talent to pick from.
Landing a Job: MOOCs aren’t just famous for their technically up-to-date courses, exclusive forums and mentorship from industry veterans – they also provide a great career support and in some cases, MOOC providers will walk the extra mile to ensure employability. Next, be on the lookout for projects that need the new skills, find work as an ML researcher and after you finish the project, make sure you to produce outcomes that you can discuss in future job interviews.
Remember, when you are ready to go for job interview, make sure you have a very strong portfolio of work to showcase. Make sure that your github repo has a range of completed deep learning projects, backed by applications and implementations of papers. Writing blog posts about each project will also help in demonstrating the methodology and the results. During the interview, try to keep the conversation on how to use deep learning for their business problems, for eg. image similarity based on the hidden layers.
Try deep learning using MATLAB