What you'll learn

A comprehensive course for beginners who want to step into the world of data science and ML.



⭐️ The three main pillars of data science and ML - Programming, Math, and Statistics.


⭐️ How to set up a python environment for Data Science / ML with Anaconda


⭐️ Everything from basic data structures to data extraction using python programming.


⭐️ How to work with essential data libraries: NumPy, Pandas, Matplotlib, and Seaborn.


⭐️ How linear algebra and calculus underpin the training of ML models.


⭐️ How Statistics enables you to describe data and quantify uncertainty in an experiment.


⭐️ All pre-requisites and pre-work before starting Google’s(or any) ML program.


Course Outline


 

Course Curriculum


To make sure this course aligns well with your learning plan, you can start learning for free right now by checking out the lessons that are marked free for preview. More lectures coming soon!

  Welcome to the course
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  Environment setup for coding in Python
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  Introduction to Programming in Python
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  Introduction to NumPy
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  Pandas - How to work with real-world data
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  Data Visualisation with Matplotlib & Seaborn
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  Basics of Algebra
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  Essential Linear Algebra for Machine Learning
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  Calculus for ML and DL
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  Descriptive Statistics for Data Science
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To help you decide

Take the course if...


✓ You are looking for the first steps to start your career in data science or ML.

✓ You want to learn how math and statistics when paired with programming drive ML algorithms.

✓ You’ve struggled to find a resource that covers all the basic foundations of data science with practical examples and projects.

✓ You want to learn to work with all the essential Python libraries for Data Analysis, Cleaning, and Visualization.



Don't take it if...


✖️ You are looking for a quick way to hack into data science.


✖️ You think ML or data science is simply using libraries and pre-defined functions.


✖️ You aren’t serious about a long-lasting successful career in the field.


Top challenges with learning Data Science & Machine Learning

It is hard to be a successful Data Scientist or ML Engineer without understanding the three main founding pillars - Math, Statistics, and Programming.

Every good data science or ML course in the market asks for a good understanding of Python programming, linear algebra, calculus, etc. but there is no course that explains why you need these and how they are used.

Most folks go on a snooze fest😴 when learning theoretical concepts of Math and Statistics.

How this course solves these challenges for you

This course covers all the pre-requisites and prework listed by the official Google’s ML course and more.

I am delivering this course to not only get you started in data science but to give you a strong foundation that will further help you in breaking down hard problems.

To save you from boredom, I have added interesting examples, quality videos, and practical code-based verifications that will keep you engaged.

Pricing - The most cost-effective course on Internet!

$39

Discounted Launch Price

Foundations of Data Science & Machine Learning

This price is only available till 21st September.

For students in India 👉


You can pay via UPI or cards using this Razorpay link.

Make sure you add the registered email address while making the payment.

You will have access to the course within 24 hours of the payment. For any queries, write to [email protected]

To clarify your doubts

Answers to some of your questions


Are there any prerequisites for this course?

A computer (Windows/Mac/Linux). You must know basic school-level arithmetics. That's it! No previous coding experience is needed. All tools and software used in this course will be free.

Who is this course for?

  • An aspiring data professional who is looking to build some solid foundations for data science.
  • You are aiming to become a Data Analyst, Data Scientist, ML/DL Practitioner.
  • You want to learn how to analyze data with python, pandas, numpy, and matplotlib.
  • You want to learn how linear algebra and calculus train ML models.

Is it even necessary to learn math and statistics?

We feel you do need to have a decent understanding of how math drives data algorithms. You don't need to be a Gold Medalist. But without understanding the working of an algorithm(which is mostly math-based), you'll have a hard time growing beyond a certain level.

I hate math and I find it boring. Is there a workaround?

I have tried my best to make the lectures as intuitive as possible. Not only have I taught the concept, but I also walk you through real-world examples of where it is used in Python.

Do you provide a certificate after completion?

Yes, we definitely do.

Can I download the videos?

Definitely. You can download any and all lessons for personal use. I understand how important time is when you are commuting, flying, or in a poor network zone.

Why are you using Python?

Because I haven't found a more versatile and easy-to-learn language that gets the job done.

How long will I have access to the course?

You have access to the course for at least 2 years. You'll need to renew it after 2 years if you'll still need it.