Welcome to 2022. I hope you all are doing well. While we are back to the office work, just wanna share that we have successfully completed 60 days of Data Science and Machine Learning Series with projects on Dec 31st, 2021. This post summarizes what we have covered and implemented in the series( by each day) So sit tight and read on!
Day wise summary of what we have completed till now and Projects -
Day 1 : Python Basics with Code Implementation - Part 1
In this post we covered end to end Python Basics ( Part 1) that you should know. Topics like data types, strings, operators, and Chaining Comparison Operators with Logical Operators are covered.
Day 2: Python Basics with Code Implementation - Part 2
In this post we covered end to end Python Basics ( Part 2) that you should know. Topics like Python Lists and Dictionaries, Sets, Tuples etc are covered in detail.
Day 3: Python Basics with Code Implementation - Part 3
In this post we covered end to end Python Basics ( Part 3) that you should know. Topics like Tuples, Sets, Loops, Break and Continue Statements, Object-Oriented Programming and Class and attributes in Python are covered in detail.
Day 4: Intermediate Python with Code Implementation - Part 1
In this post we covered end to end Intermediate Python ( Part 1) that you should know. Topics like First Class functions, Private Variables, Global and Non Local Variables, import function, Magic Functions, Tuple Unpacking, Static Variables and Methods in Python are covered in detail.
Day 5: Intermediate Python with Code Implementation - Part 2
In this post we covered end to end Intermediate Python( Part 2) that you should know. Topics like Lambda Functions, Magic methods, Inheritance and Polymorphism, Errors and Exception Handling, User-defined functions, Python garbage collection, and debugger are covered in detail.
Day 6 : Advanced Python with Code Implementation
In this post we covered end to end Advanced Python that you should know. Topics like Decorators, Memoization using Decorators, Generators, Ordered and Defaultdict, Coroutine with Code implementation are covered in detail.
Day 7– Statistics for Data Science and Machine Learning with Code Implementation
In this post we covered Statistics for Data Science you should know.
Day 8 - Maths for Data Science and Machine learning
In this post we covered Maths for ML . Topics like Linear Algebra, Calculus, Matrix and Vectors, Bayes Theorem and Cheatsheets etc are covered in detail.
Day 9 : Pandas Part 1 with Code Implementation
In this post we covered Pandas part 1 in depth with Code Implementation. Pandas is an open source Python package written for the Python programming language for data manipulation, analysis and ML tasks.
Day 10: Pandas Part 2 with Code Implementation
In this post we covered Pandas part 2 in depth with Code Implementation. Topics like indexing, filtering, transformation, Merging, Hierarchical Indexing etc are covered.
Day 11 : Numpy with Code Implementation
In this post we covered Numpy part 1 with focus on Flattening the arrays, Concatenation and Broadcasting etc in detail. Numpy is a python library for scientific computing - to work with multidimensional array objects and used to handle large amount of data. An array which is a grid of values and is indexed by a tuple of nonnegative integers is main data structure of the Numpy library.
Day 12 : Data Pre-processing Part 1 with Code Implementation
In this post we learned/implemented Hands on Data Pre-processing in depth - Part 1. Data preprocessing , one of the first and crucial step - the process in which we prepare the raw data and make it suitable for a ML model to increase its accuracy and efficiency.
Day 13 : Data Pre-processing Part 1 with Code Implementation
In this post we learned/implemented Hands on Data Pre-processing in depth - Part 2. Topics like Data Cleaning, Data Augmentation, Transformation, Channel Shift etc are covered in detail.
Day 14 : Regression Part 1 with Code Implementation
In this post where we learned/implemented Hands on Regression in depth - Part 1. Topics like Simple Linear Regression, Multi Linear Regression, Polynomial Regression are covered in detail.
Day 15 : Regression Part 2 with Code Implementation
In this post where we learned/implemented Hands on Regression in depth - Part 2. Topics like Support Vector Regression, Decision Tree Regression and Random Forest Regression are covered in detail.
Day 16 : Reflect and Connect the dots
In this we covered various Data Science and ML projects.
Day 17 : Project - Kaggle's annual Machine Learning and Data Science Survey ( Part 1 )
In this post we implemented a project and covered some of the most important concepts - data cleaning, preprocessing, EDA etc through a project.
You can get the dataset for this project from my Github repo(thanks to Kaggle ) -
github.com/Pikachu0405/Kaggle-2021-survey-p..
This data ( Kaggle's annual Machine Learning and Data Science Survey) has 42+ questions and 25,973 responses and for this post we will cover how to approach a problem and a very elementary view covering how to analyze your data.
Day 18 : Project - DecisionTreeRegressor and RandomForestRegressor
In this post we developed an intuition and implemented DecisionTreeRegressor and RandomForestRegressor through a project.
Day 19 : Project - Kaggle's annual Machine Learning and Data Science Survey ( Part 2 )
In this post we covered second part of the Kaggle's annual Machine Learning and Data Science Survey project.
Day 20: Project - Detailed Crypto Analysis
In this post we covered detailed Crypto Analysis to build a basic intuition and part 2 covers how we can build a model to predict the prices.
Day 21: Project - Detailed Analysis of the Netflix Content.
In this post we covered detailed Analysis of the Netflix Content.
Day 22 : All the Important ML algorithms with projects
This post covered a quick overview of ML algorithms with projects.
Day 23 : Machine Learning Classification and a Project
In this post we covered ML Classification in detail with a project.
Day 24 : Machine Learning Classification Project 2 ( Part 1)
In this post we covered ML Classification on Customer Review and Analysis in detail with another project ( Part 1).
Day 25 : Machine Learning Classification Project 2 ( part 2)
In this post we covered ML Classification on Customer Review and Analysis in detail with another project ( Part 2).
Day 26 : Machine Learning Clustering in detail with a project 1
In this post we covered Machine Learning Clustering in detail with a project( Part 1).
Day 27 : Machine Learning Clustering in detail with a project 1
In this post we covered Machine Learning Clustering in detail with a project( Part 2).
Day 28 : Machine Learning Clustering in detail with a project 2 ( part 1)
In this post we covered Machine Learning Clustering in detail with another project( Part 1).
Day 29 : Machine Learning Clustering in detail with a project 2 ( part 2)
In this post we covered Machine Learning Clustering in detail with another project( Part 2).
Day 30: Machine Learning Clustering in detail with a project 2 ( part 3)
In this post we covered Machine Learning Clustering in detail with another project( Part 3).
Day 31: Machine Learning Regression in detail with a project
In this post we covered univariate linear regression with a project.
Day 32: Multiple linear regression with a project
In this post we covered multiple linear regression with a project. Along the lines we evaluated model fit and accuracy using numerical measures such as R² and RMSE.
Day 33 : Logistic regression with a project
In this post we covered logistic regression with a project.
Day 34 : Logistic regression with another project
In this post we covered logistic regression with another project.
Day 35 : Principal Component Analysis with a project
In this post we covered Principal Component Analysis with a project.
Day 36 : Advanced Regression Techniques with project ( Part 1)
In this post we covered Advanced Regression Techniques with a project
Day 37 : Advanced Regression Techniques with project ( Part 2)
In this post we covered Advanced Regression Techniques with a project
Day 38 : Support Vector Machine with a project
In this post we covered Support Vector Machine with a project
Day 39 : Scikit learn with a project
In this post we covered the basics of Scikit learn with a project.
Day 40 : Tensorflow with a project
In this post we covered the basics of Tensorflow with a project.
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Day 41 : Neural Network with a project
In this post we covered the basics of Neural Network with Tensorflow with a project.
Day 42 : RNN and Tensorflow with a project
In this post we covered the basics of RNN and Tensorflow with a project.
[Day 43: Regression using Tensorflow with a project](medium.com/coders-mojo/day-43-60-days-of-da..
In this post we covered Regression using Tensorflow with a project
Day 44: Long Short Term Memory networks (LSTM) with Keras
In this post we covered the basics of Long Short Term Memory networks (LSTM) with Keras through a project
Day 45 : Recurrent Neural Network with a project
In this post we covered the basics of Recurrent Neural Network with a project
Day 46 : Language Classification with a project
In this post we covered the basics of Multinomial Naive Bayes through a project.
Day 47 : RNN and LSTM with a project
In this post we covered the basics of RNN and LSTM with a project
Day 48 : Multilayer Perceptron with project
In this project we implemented a multilayer Perceptron model with Keras.
In this post, we analyzed the text data using Yellowbrick and assess document similarity, topic modeling etc that are predicated on the notion of "similarity" between documents.
Day 50 : Bidirectional Encoder Representations from Transformers ( BERT) with a project
In this post we learned how to fine tune BERT for text classification.
Day 51 : Yellowbrick with a project
In this project we implemented visualization using yellowbrick
Day 52 : Yellowbrick with 2nd project
In this project we implemented visualization using yellowbrick through a project
Day 53 : Yellowbrick with 3rd project
In this project we implemented visualization using yellowbrick through a project
Day 54 : Pytorch and ResNet with a project
In this post we learned about the basics of PyTorch ( one of my favorite library) and ResNet.
Day 55 : Natural Language Processing using Naive Bayes through a project
In this post we learned and implemented the basics of NLP using Naive Bayes through a project.
Day 56 : ANN, Linear Regression, Decision Tree Regression and Random Forest with a project
In this post we covered ANN, Linear Regression, Decision Tree Regression and Random Forest with a project
Day 57 : Deep learning and BERT
In this post we learned how to perform sentiment analysis using BERT.
Day 58 : RNN and LSTM through a project
In this post we covered the basics of RNN and LSTM through a project.
Day 59 : Natural Language Processing and Convolutions
In this post we learned and implemented 1D Convolutions as Feature Extractors for Text in NLP.
Day 60 : Transfer learning and Text Classification
In this project we learned and implemented how to use transfer learning to fine-tune models, use pre-trained NLP text embedding models from TensorFlow Hub.
What's Next?
- 30 days of MLOps
- 30 days of NLP
Follow for more updates. Stay tuned and keep coding!