Foundations of Data Science & Machine Learning Download For Free
What you’ll 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
Requirements
- 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.
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
- 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.
Who this course is for:
- Anyone looking to get into data science or ML. This is where one should start.