| 1. Anaconda – An Overview & Installation.mp4 | 11.4 MB | ||
| 1. Congratulatory and Closing Note.mp4 | 24.7 MB | ||
| 1. Course Contents.mp4 | 21.3 MB | ||
| 1. Important Libraries – An Overview.mp4 | 43.8 MB | ||
| 1. Introduction to Machine Learning.mp4 | 84.3 MB | ||
| 1. JupyterLab – An Overview.mp4 | 20.1 MB | ||
| 1. Linear Algebra – An Overview.mp4 | 93.2 MB | ||
| 1. OOPs – An Overview.mp4 | 61.8 MB | ||
| 1. Probability – An Overview.mp4 | 91.2 MB | ||
| 1. Python Data Types & Structures, NumPy Data Structures.mp4 | 121.1 MB | ||
| 1. Statistics – An Overview.mp4 | 159.1 MB | ||
| 1. Welcome Message.mp4 | 29.6 MB | ||
| 10. [Hands on 5] Handling missing data.mp4 | 54.2 MB | ||
| 10.1 Python_Overview_Hands_on_5.ipynb | 10.6 KB | ||
| 11. Hierarchical Indexing Multi-Indexing.mp4 | 27.7 MB | ||
| 12. [Hands on 6] Hierarchical Indexing Multi-Indexing.mp4 | 187.6 MB | ||
| 12.1 Python_Overview_Hands_on_6.ipynb | 22.3 KB | ||
| 13. Combining Datasets.mp4 | 29.2 MB | ||
| 14. [Hands on 7] Combining Datasets.mp4 | 273.7 MB | ||
| 14.1 Python_Overview_Hands_on_7.ipynb | 80.3 KB | ||
| 15. Aggregation and Grouping.mp4 | 42.8 MB | ||
| 16. [Hands on 8] Aggregation and Grouping.mp4 | 204 MB | ||
| 16.1 Python_Overview_Hands_on_8.ipynb | 21 KB | ||
| 17. Strings, List-Set-Dictionary Comprehensions, Functions, Unpacking Sequence.mp4 | 83.5 MB | ||
| 18. [Hands on 9] Strings, List-Set-Dictionary Comp., Functions, Unpacking Seqence.mp4 | 192.6 MB | ||
| 18.1 Python_Overview_Hands_on_9.ipynb | 27.5 KB | ||
| 2. [Hands on 1] Python Data Types & Structures, NumPy Data Structures.mp4 | 546.8 MB | ||
| 2. [Hands on] JupyterLab Overview (Notebook Commands, Markdown Codes).mp4 | 53.1 MB | ||
| 2.1 Jupyter_Notebook_Hands_on.ipynb | 13.7 KB | ||
| 2.1 Python_Overview_Hands_on_1.ipynb | 60.4 KB | ||
| 2.2 LaTeX_Symbols.png | 43.1 KB | ||
| 3. Pandas Data Structures.mp4 | 24.3 MB | ||
| 4. [Hands on 2] Pandas Data Structures.mp4 | 181.6 MB | ||
| 4.1 olympics.csv | 8.2 KB | ||
| 4.2 Python_Overview_Hands_on_2.ipynb | 26 KB | ||
| 5. Pandas Data Indexing and Selection.mp4 | 31.9 MB | ||
| 6. [Hands on 3] Pandas Data Indexing and Selection.mp4 | 75.1 MB | ||
| 6.1 Python_Overview_Hands_on_3.ipynb | 14.4 KB | ||
| 7. Pandas Operating on Data.mp4 | 9.8 MB | ||
| 8. [Hands on 4] Pandas Operating on Data.mp4 | 69.6 MB | ||
| 8.1 Python_Overview_Hands_on_4.ipynb | 21.3 KB | ||
| 9. Handling missing data.mp4 | 18.4 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 44 total files | |||
A Foundation For Machine Learning and Data Science
https://DevCourseWeb.com
Published 1/2024
Created by Balasubramanian Chandran
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 30 Lectures ( 6h 47m ) | Size: 2.79 GB
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
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
|
Udemy - ETF Investing 101 - A Beginner's Guide to Building Long-Term Posted by
freecoursewb in Other
|
1.1 GB | freecoursewb | 1 day | 0 | 0 |
| 494 MB | freecoursewb | 1 day | 0 | 0 | |
|
Udemy - Becoming an Agent Manager - Building a Frontier Organization Posted by
freecoursewb in Other
|
287.4 MB | freecoursewb | 2 days | 0 | 0 |
| 482.6 MB | freecoursewb | 2 days | 34 | 9 | |
| 2.6 GB | freecoursewb | 2 weeks | 13 | 5 |
All Comments