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.
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.