Foundations of Data Science & Machine Learning

Essentials of Programmning, Mathematics and Statistics to get started with Data Science and Machine Learning.

Ratings: 4.79 / 5.00




Description

To have a successful, long-lasting career in Data Science or Machine Learning, you'll need a solid understanding of the three pillars of DS and ML namely, Programming, Math, and Statistics.

The course is based on Google's recommendations before starting any ML course.

It is a comprehensive yet compact course that not only covers all the essentials, pre-requisites, & pre-work but also explains how each concept is used computationally and programmatically (python).

We follow the following path in this course:

  • Learn to set up a professional python environment

  • Learn to program in python using fundamental data structures and methods.

  • Learn to work with data science libraries

  • NumPy for Multidimensional Arrays

  • Pandas for Data Manipulation

  • Matplotlib and Seaborn for Data Visualization

  • Basics of Algebra - From variables to all important functions

  • Linear Algebra for Machine Learning - data representation, vector norms, solving linear regression problems.

  • Calculus that trains ML models - learn how gradient descent works to minimize the loss function.

  • Training a linear regression model from scratch without using any ML package

  • Statistics, data distributions, and basics of probability

After completing this course, you'll be ready to straight away start working on:

  • Data Analysis projects

  • Pick up any ML course

  • Start with a Data Science course

  • Start with the Predictive analytics course

  • Enroll for any fast-paced Bootcamp course after covering all the basics.

What You Will Learn!

  • Learn the essentials - the three main pillars of data science and ML - Programming, Math, and Statistics.
  • Everything from basic data structures to data extraction using python programming. Learn to work with 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.
  • Cover all pre-requisites and pre-work before starting any Google’s(or any) data science or ML program.
  • Build models from scratch, learn the math behind, program

Who Should Attend!

  • Anyone looking to get into data science or ML. This is where one should start.