A Foundation For Machine Learning and Data Science

A solid foundational course for ML and Data Science with Python, Linear Algebra, Statistics, Probability, and OOPs.

What you'll learn
A solid foundation for Machine Learning and Data Science
Black-box ML concepts
A high-level understanding of the 11 stages involved in developing and implementing ML projects
Python for Machine Learning and Data Science
Python data types and structures, NumPy data structures, and Pandas data structures
Pandas data indexing and selection, Operating on Pandas data, Handling missing data, Hierarchical indexing/ multi-indexing
Combining datasets, aggregation, and grouping
Working with strings, list-set-dictionary comprehensions, functions, unpacking sequences, and so on
How to use NumPy for numerical computing, vectorization, broadcasting, data transformation, and so on
How to use Pandas for data analysis and data manipulation
Jupyter Notebook commands and markdown codes
Linear algebra including the types of linear regression problems and the types of classification problems, and so on
Statistics including Why do we need to learn statistics? What are statistical models? What are the different types of statistics available?
What are mean, median, mode, quartiles, and percentiles? What are range, variance, and standard deviation? What are skewness and kurtosis?
What are the different types of variables we will be dealing with?
How statistics is used in various stages of machine learning? and so on
Probability theory including the language of Probability theory, Probability Tree, Types of probability, why we need to learn Probability? and so on
Object-Oriented Programming
An overview of important libraries used in ML and DS for data processing, data analysis, data manipulation, visualization, and other supporting libraries
And, much more


Requirements
Fundamentals of computer science and programming
High school-level basic mathematics
Description
This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.
The course will equip students with a solid understanding of the theory and practical skills necessary to learn machine learning models and data science.
When building a high-performing ML model, it’s not just about how many algorithms you know; instead, it’s about how well you use what you already know.
Throughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.
This course contains 9 sections:
1. Introduction to Machine Learning
2. Anaconda – An Overview & Installation
3. JupyterLab – An Overview
4. Python – An Overview
5. Linear Algebra – An Overview
6. Statistics – An Overview
7. Probability – An Overview
8. OOPs – An Overview
9. Important Libraries – An Overview
This course includes 20 lectures, 10 hands-on sessions, and 10 downloadable assets.
By the end of this course, I am confident that you will outperform in your job interviews much better than those who have not taken this course, for sure.
Who this course is for:
Beginners with little programming experience and basic mathematics
Experienced programmers who want to pursue a career in ML/ Data Science/ AI
People who have already taken other Machine Learning and Data Science courses who want to strengthen their foundational skills


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