Day 1 — Day 60 : Quick Recap of 60 days of Data Science and ML
Connect the ML dots…
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)
Projects Videos —
All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).
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Tech Newsletter —
If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :
Solved System Design Case Studies — In Depth
Design Instagram
Design Netflix
Design Reddit
Design Amazon
Design Messenger App
Design Twitter
Design URL Shortener
Design Dropbox
Design Youtube
Design API Rate Limiter
Design Web Crawler
Design Amazon Prime Video
Design Facebook’s Newsfeed
Design Yelp
Design Uber
Design Tinder
Design Tiktok
Design Whatsapp
Most Popular System Design Questions
Mega Compilation : Solved System Design Case studies
Complete Data Structures and Algorithm Series
Github —
Advanced SQL Series
Day 2 : SQL Basics, Query Structure, Built In functions Conditions
Day 4 : Set Theory Operations, Stored Procedures and CASE statements in SQL
Day 6 : Subqueries, Group by, order by and Having clauses in SQL and Analytical Functions
Day 7 : Window Functions, Grouping Sets and Constraints in SQL
Day 8 : BigQuery Basics, SELECT, FROM, WHERE and Date and Extract in BigQuery
Day 9 : Common Expression Table, UNNEST Clause, SQL vs NoSQL Databases
Day 10 : Triggers, Pivot and Cursors in SQL
Day 14 : MySQL in Depth
Day 15 : PostgreSQL inDepth
Some of the other best Series —
How to solve any System Design Question ( approach that you can take)?
30 days of Data Structures and Algorithms and System Design Simplified
100 days : Your Data Science and Machine Learning Degree Series with projects
Complete Data Visualization and Pre-processing Series with projects
Tech Newsletter —
If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :
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.
Where to find Day 1 post :
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.
Where to find Day 2 post :
Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) —
Find best data science and data engineering courses here
Find best Machine Learning and Deep Learning courses here
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.
Where to find Day 3 post :
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.
Where to find Day 4 post :
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.
Where to find Day 5 post :
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.
Where to find Day 6 post :
Day 7– Statistics for Data Science and Machine Learning with Code Implementation
In this post we covered Statistics for Data Science you should know.
Where to find Day 7 post :
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.
Where to find Day 8 post :
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.
Where to find Day 9 post :
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.
Where to find Day 10 post :
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.
Where to find Day 11 post :
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.
Where to find Day 12 post :
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.
Where to find Day 13 post :
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.
Where to find Day 14 post :
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.
Where to find Day 15 post :
Day 16 : Reflect and Connect the dots
In this we covered various Data Science and ML projects.
Where to find Day 16 post :
Day 16–60 days of Data Science and Machine Learning
Connect the docs..
medium.datadriveninvestor.com
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 ) —
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.
Where to find Day 17 post :
Day 18 : Project — DecisionTreeRegressor and RandomForestRegressor
In this post we developed an intuition and implemented DecisionTreeRegressor and RandomForestRegressor through a project.
Where to find Day 18 post :
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.
Where to find Day 19 post :
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..
Where to find Day 20 post :
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 39: 60 days of Data Science and Machine Learning Series
Scikit learn with a project..
medium.com
Day 40 : Tensorflow with a project
In this post we covered the basics of Tensorflow with a project.
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
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.
Day 49 : Yellowbrick for NLP
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.
All the Complete System Design Series Parts —
6. Networking, How Browsers work, Content Network Delivery ( CDN)
Github —
30 days of Data Analytics Series —
Day 1 : Data Analytics basics and kickstart of Data analytics with projects series
Day 3 : Data Analytics Ecosystem — Data Life Cycle, Data Analysis complete process ( most important things)
Day 5 : Statistics
Day 6 : Basic and Advanced SQL
Day 8 : Pandas and Numpy
Day 9 : Data Manipulation
Day 10 : Data Visualization — Part 1
Day 11 : Project 1 : Data Visualization — Part 2
Day 12 : Data Visualization — Part 3
Day 13: Tableau — Part 1
Day 14: Tableau — Part 2
Day 15: Tableau — Part 3
Day 16 : Data Analysis Project 2
Day 17 : Data Analysis Project 3
Day 18: Data Analysis Project 4
Day 20 : Data Analysis Project 6
Day 21 : Data Analysis Project 7
Take Complete Hands On Tableau Course : Link
Advanced SQL Series
Day 2 : SQL Basics, Query Structure, Built In functions Conditions
Day 4 : Set Theory Operations, Stored Procedures and CASE statements in SQL
Day 6 : Subqueries, Group by, order by and Having clauses in SQL and Analytical Functions
Day 7 : Window Functions, Grouping Sets and Constraints in SQL
Day 8 : BigQuery Basics, SELECT, FROM, WHERE and Date and Extract in BigQuery
Day 9 : Common Expression Table, UNNEST Clause, SQL vs NoSQL Databases
Day 10 : Triggers, Pivot and Cursors in SQL
Day 14 : MySQL in Depth
Day 15 : PostgreSQL inDepth
Anyways, For Day 15 of 15 days of Advanced SQL, we will cover —
PostgreSQL inDepth
Github for Advanced SQL that you can follow —
All the projects, data structures, algorithms, system design, Data Science and ML, Data Engineering, MLOps and Deep Learning videos will be published on our youtube channel ( just launched).
Subscribe today!
System Design Case Studies — In Depth
Complete Data Structures and Algorithm Series
Github —
For other projects, tune to —
Build Machine Learning Pipelines( With Code)
Recurrent Neural Network with Keras
Clustering Geolocation Data in Python using DBSCAN and K-Means
Facial Expression Recognition using Keras
Follow for more updates. Stay tuned and keep coding! Some of the links are affiliates.
Hyperparameter Tuning with Keras Tuner
Custom Layers in Keras
