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TORRENT DETAILS
Udemy - A Foundation For Machine Learning And Data Science
TORRENT SUMMARY
Status:
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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
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FILE LIST
Filename
Size
~Get Your Files Here !/1. Welcome Message/1. Welcome Message.mp4
29.6 MB
~Get Your Files Here !/10. OOPs – An Overview/1. OOPs – An Overview.mp4
61.8 MB
~Get Your Files Here !/11. Important Libraries – An Overview/1. Important Libraries – An Overview.mp4
43.8 MB
~Get Your Files Here !/12. Congratulatory and Closing Note/1. Congratulatory and Closing Note.mp4
24.7 MB
~Get Your Files Here !/2. Course Contents/1. Course Contents.mp4
21.3 MB
~Get Your Files Here !/3. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4
84.3 MB
~Get Your Files Here !/4. Anaconda – An Overview & Installation/1. Anaconda – An Overview & Installation.mp4
11.4 MB
~Get Your Files Here !/5. JupyterLab – An Overview/1. JupyterLab – An Overview.mp4
20.1 MB
~Get Your Files Here !/5. JupyterLab – An Overview/2. [Hands on] JupyterLab Overview (Notebook Commands, Markdown Codes).mp4
53.1 MB
~Get Your Files Here !/5. JupyterLab – An Overview/2.1 Jupyter_Notebook_Hands_on.ipynb
13.7 KB
~Get Your Files Here !/5. JupyterLab – An Overview/2.2 LaTeX_Symbols.png
43.1 KB
~Get Your Files Here !/6. Python Overview/1. Python Data Types & Structures, NumPy Data Structures.mp4
121.1 MB
~Get Your Files Here !/6. Python Overview/10. [Hands on 5] Handling missing data.mp4
54.2 MB
~Get Your Files Here !/6. Python Overview/10.1 Python_Overview_Hands_on_5.ipynb
10.6 KB
~Get Your Files Here !/6. Python Overview/11. Hierarchical Indexing Multi-Indexing.mp4
27.7 MB
~Get Your Files Here !/6. Python Overview/12. [Hands on 6] Hierarchical Indexing Multi-Indexing.mp4
187.6 MB
~Get Your Files Here !/6. Python Overview/12.1 Python_Overview_Hands_on_6.ipynb
22.3 KB
~Get Your Files Here !/6. Python Overview/13. Combining Datasets.mp4
29.2 MB
~Get Your Files Here !/6. Python Overview/14. [Hands on 7] Combining Datasets.mp4
273.7 MB
~Get Your Files Here !/6. Python Overview/14.1 Python_Overview_Hands_on_7.ipynb
80.3 KB
~Get Your Files Here !/6. Python Overview/15. Aggregation and Grouping.mp4
42.8 MB
~Get Your Files Here !/6. Python Overview/16. [Hands on 8] Aggregation and Grouping.mp4
204 MB
~Get Your Files Here !/6. Python Overview/16.1 Python_Overview_Hands_on_8.ipynb
21 KB
~Get Your Files Here !/6. Python Overview/17. Strings, List-Set-Dictionary Comprehensions, Functions, Unpacking Sequence.mp4
83.5 MB
~Get Your Files Here !/6. Python Overview/18. [Hands on 9] Strings, List-Set-Dictionary Comp., Functions, Unpacking Seqence.mp4
192.6 MB
~Get Your Files Here !/6. Python Overview/18.1 Python_Overview_Hands_on_9.ipynb
27.5 KB
~Get Your Files Here !/6. Python Overview/2. [Hands on 1] Python Data Types & Structures, NumPy Data Structures.mp4
546.8 MB
~Get Your Files Here !/6. Python Overview/2.1 Python_Overview_Hands_on_1.ipynb
60.4 KB
~Get Your Files Here !/6. Python Overview/3. Pandas Data Structures.mp4
24.3 MB
~Get Your Files Here !/6. Python Overview/4. [Hands on 2] Pandas Data Structures.mp4
181.6 MB
~Get Your Files Here !/6. Python Overview/4.1 olympics.csv
8.2 KB
~Get Your Files Here !/6. Python Overview/4.2 Python_Overview_Hands_on_2.ipynb
26 KB
~Get Your Files Here !/6. Python Overview/5. Pandas Data Indexing and Selection.mp4
31.9 MB
~Get Your Files Here !/6. Python Overview/6. [Hands on 3] Pandas Data Indexing and Selection.mp4
75.1 MB
~Get Your Files Here !/6. Python Overview/6.1 Python_Overview_Hands_on_3.ipynb
14.4 KB
~Get Your Files Here !/6. Python Overview/7. Pandas Operating on Data.mp4
9.8 MB
~Get Your Files Here !/6. Python Overview/8. [Hands on 4] Pandas Operating on Data.mp4
69.6 MB
~Get Your Files Here !/6. Python Overview/8.1 Python_Overview_Hands_on_4.ipynb
21.3 KB
~Get Your Files Here !/6. Python Overview/9. Handling missing data.mp4
18.4 MB
~Get Your Files Here !/7. Linear Algebra – An Overview/1. Linear Algebra – An Overview.mp4
93.2 MB
~Get Your Files Here !/8. Statistics – An Overview/1. Statistics – An Overview.mp4
159.1 MB
~Get Your Files Here !/9. Probability – An Overview/1. Probability – An Overview.mp4
