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Keep shoppingA Foundation For Machine Learning and Data Science
11 students
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.
Created byBalasubramanian Chandran
Last updated 1/2024
English
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
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Course content
12 sections • 30 lectures • 6h 47m total length
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