Foundations of Data Science & Machine Learning

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.

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