Introducing “PocketML” – an Experiential Learning Platform for Data Science
Do we need to change the way we learn?
Before you read any further, here is a sneak peak of our new experiential learning product – PocketML.
What is PocketML?
After hosting thousands of offline trainings, writing blogs on a variety of topics and designing online training, we have realized 3 ingredients that differentiate great trainings from normal ones:
- Focussed content with a clear flow of ideas
- Interaction with learners to make the process fun
- Concise content for it to be consumed while you are on the move, specially for the Mobile generation.
We thought let’s try to get all 3 right in 1 product and that is what you just experienced in the previous section.
Introducing PocketML as in “Machine Learning in your Pocket”. In upcoming sections of this article, you will find the first 4 modules on Python using the learning methodology of PocketML.
Python Basics
Let’s start with the Python basics first.
Conditionals in Python
Now that you are comfortable with Python, it’s time to learn about conditionals. Conditional statements like “if” and “else” give more power to you as a data scientist as you can define and check multiple conditions on your data itself.
Loops in Python
Loops are used when you want to repeat a set of instructions. They are a data scientist’s friend when you want to perform data manipulation through rows of data! Click below to learn more about loops.
Functions in Python
Functions are used to organize your Python code so that you can reuse it whenever needed. Here is how functions work:
While we still actively work on traditional online course formats, we believe the format of PocketML enables the community to maximize the learning capacity.
We see both the formats of learning complement each other extremely well. PocketML gets you excited about new topics/platforms/subjects and traditional module makes you go in-depth of this topic.
Your turn to tell us!
Loved it? Liked it? Needs improvement? Did you realize how engaging this format is compared to traditional online learning modules?
We will love to hear from you so that we can improve before we launch PocketML. What you just learned is all you need to know to start using Python.
Give us your feedback in the comments section below!
Kunal is a post graduate from IIT Bombay in Aerospace Engineering. He has spent more than 10 years in field of Data Science. His work experience ranges from mature markets like UK to a developing market like India. During this period he has lead teams of various sizes and has worked on various tools like SAS, SPSS, Qlikview, R, Python and Matlab.
Though I know python programming, yet I wanted to try PocketML; this approach is beginner friendly and great way to start python programming especially for those who are learning to code. Hope you guys also include intermediate python topics such as lists, numpy and pandas in details using the same approach. PocketML should also contain byte sized lessons for learning statistical learning, Machine Learning, NLP and deep learning algorithms, however, I think AV team has already planned for it and maybe we get to experience new content in a future update of the product! 😊 Best wishes on your new product launch 👏
Thanks Priyankita for the encouraging words. We are glad you liked the experience of Pocket ML. We have other complex concept Pocket ML in pipeline. Stay tuned.
I think this looks promising. Does it come with the capability to use in a separate browser tab where there is a url that runs pocketML and you can possibly play around with it?
Hi Pranay, At this point we have launched Pocket ML integrated with blogs. However, later in our product journey, we will enable usage of pocket ML independent of the blog. Stay tuned.
I liked it. Mobile friendly and easy to stop/start at any point.
Cool. Great start for beginners! The answer is not showing up for the question The values within the parenthesis are called “Parameters” of the function. Identify the parameters from the code below: print("The value of the string is",p).
Hi Kunal , You and your team never stops to amaze the AV fans. While I was still in Datamin's (Question Quality) hangover , there came another dose. This is really fun and I hope to see the real juice coming in when ML related topics start flowing in.
Great experiment to gamify the learning, it will work wonders.
this is great and engaging. probably some improvements needed? no content rendered for some navigations of "functions in python".
Hi Kunal & Team, YES, this is really interesting and keeping the learner engaged. I am sure,with the launch of "PocketML" learners will complete a whole chunk or two in ONE GO. This is engaging and encouraging as well. Keep up the good work and looking forward to "PocketML".
Its amazing, waiting for the final product to be launched.