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.

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.

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.

Course Content

  • Welcome Message –> 1 lecture • 4min.
  • Course Contents –> 1 lecture • 4min.
  • Introduction to Machine Learning –> 1 lecture • 12min.
  • Anaconda – An Overview & Installation –> 1 lecture • 2min.
  • JupyterLab – An Overview –> 2 lectures • 12min.
  • Python Overview –> 18 lectures • 4hr 56min.
  • Linear Algebra – An Overview –> 1 lecture • 15min.
  • Statistics – An Overview –> 1 lecture • 27min.
  • Probability – An Overview –> 1 lecture • 13min.
  • OOPs – An Overview –> 1 lecture • 13min.
  • Important Libraries – An Overview –> 1 lecture • 7min.
  • Congratulatory and Closing Note –> 1 lecture • 2min.

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Requirements

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.