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» » » Foundations of Data Science & Machine Learning

Foundations of Data Science & Machine Learning


Foundations of Data Science & Machine Learning
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.40 GB | Duration: 20h 46m

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