README.md

60-Days-of-Data-Science-and-ML

This repository is based on the medium article series of Naina Chaturvedi. Being read her articles, I will try all exercises on there and share those script in this repository. You can find her article series here.

Notebooks

• Day 1 - Python Basics Part1

  Topic : Data Types, Strings, Operators, Chaining Comparison Operators with Logical Operators

• Day 2 - Python Basics Part2

  Topic : Python Lists and Dictionaries, Sets, Tuples and etc

• Day 3 - Python Basics Part3

  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

• Day 6 - Advanced Python

  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

• Day 9 - Pandas Part1

  Topic : Pandas Series, DataFrame

• Day 10 - Pandas Part2

  Topic : Indexing, Filtering, Transformation, Merging, Hierarchial Indexing

• Day 11 - Numpy

  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

• Day 14 - Regression Part1

  Topic : Simple Linear Regression, Multi Linear Regression, Polynomial Regression

• Day 15 - Repression Part2

  Topic : Support Vector Regression, Decision Tree Regression and Random Forest Regression

• Day 17 - Kaggle's Annual Machine Learning and Data Science Survey Part1

  Topic : Data Cleaning, Preprocessing, EDA and etc

• Day 18 - DecisionTreeRegressor and RandomForestRegressor

  Topic : Implement Regressor with Decision Tree and Random Forest

• Day 19 - Kaggle's Annual Machine Learning and Data Science Survey Part2

  Topic : Data Cleaning, Preprocessing, EDA and etc

• Day 20 - Detailed Crypto Analysis

  Topic : Basic intuition to buid model to predict

• Day 21 - Detailed Analysis of the Netflix Content

  Topic : Detailed analysis of the Netflix Content

• Day 22 - All the important ML algorithms

  Topic : Quick overview of ML algorithms

• Day 24 - Machine Learning Classification Project2 Part1

  Topic : ML Classification on Customer Review and Analysis in details

• Day 25 - Machine Learning Classification Project2 Part2

  Topic : ML Classification on Customer Review and Analysis in details

• Day 26 - Machine Learning Clustering Project1 Part1

  Topic : Machine Learning Clustering with Customer Segmentation

• Day 27 - Machine Learning Clustering Project1 Part2

  Topic : Machine Learning Clustering with Customer Segmentation

• Day 28 - Machine Learning Clustering Project2 Part1

  Topic : Machine Learning Clustering with Suctomer Personality Analysis

• Day 29 - Machine Learning Clustering Project2 Part2

  Topic : Machine Learning Clustering with Suctomer Personality Analysis

• Day 30 - Machine Learning Clustering Project2 Part3

  Topic : Machine Learning Clustering with Suctomer Personality Analysis

• Day 31 - Machine Learning Linear Regression

  Topic : Univariate linear regression

• Day 32 - Multiple Linear Regression

  Topic : Multiple linear regression

• Day 33 - Logistic Regression Project1

  Topic : Logistic Regression

• Day 35 - Principle Component Analysis

  Topic : Principle Component Analysis

• Day 36 - Advanced Regression Techniques Part1

  Topic : Advanced Regression Techniques

• Day 37 - Advanced Regression Techniques Part2

  Topic : Advanced Regression Techniques

• Day 38 - Support Vector Machine

  Topic : SVM (Support Vector Machine)

• Day 39 - Scikit-learn

  Topic : Basics of Scikit learn

• Day 40 - Tensorflow

  Topic : Basics of Tensorflow