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60 Days of Data Science and Machine Learning
Hello, everyone.
Following are first 2 weeks of this series. You can find them on my github repository. You can run all the notebook on colab or jupyter as well.
Topic : Data Types, Strings, Operators, Chaining Comparison Operators with Logical Operators
Topic : Python Lists and Dictionaries, Sets, Tuples and etc
Topic : Loop, Break and Continue Statement, Object-Oriented Programming and Class
Day 4 - Intermediate Python Part1
Topic : First Class Function, Private variables, Global and Non Local variables, Magic Function, Tuple Unpacking, Static Variables and Method
Day 5 - Intermediate Python Part2
Topic : Lambda function, Matic methods, Inheritance and Polymorphism, Erros and Exception Handling, User-defined function, Python garbage collection, and debugger
Topic : Decorators, Memoization using Decorators, Generators, Ordered and Defaultdict, Coroutine
Day 7 - Statistics for Data Science and Machine Learning
Topic : Statistics for Data Science
Day 8 - Maths for Data Science and Machine Learning
Topic : Linear Algebra, Calculus, Matrix and Vectors, Bayes Theorem and Cheatsheets
Topic : Pandas Series, DataFrame
Topic : Indexing, Filtering, Transformation, Merging, Hierarchial Indexing
Topic : Flattening, Concatenation and Broadcasting
Day 12 - Data PreProcessing Part1
Topic : Encoding categorical data, Split data, Feature Scaling
Day 13 - Data PreProcessing Part2
Topic : Data Cleaning, Data Augmentation, Transformatoin, Channel Shift
Topic : Simple Linear Regression, Multi Linear Regression, Polynomial Regression
Hope my work would be little bit helpful for AI enthusiastic.
If that, please star that repository then follow me on Github and Dev.to
https://dev.to/thunderstroke
https://github.com/thunderstroke325
Best Regards.
Original Link: https://dev.to/thunderstroke/60-days-of-data-science-and-machine-learning-57p9
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