Data Science And Machine Learning Projects — Mega Compilation Part 3
Part 1…
Welcome back peeps. This post ( part 1) is all about Data Science and Machine Learning Projects that you can build to practically understand the concepts.
Some of the other best Series —
How to solve any System Design Question ( approach that you can take)?
100 days : Your Data Science and Machine Learning Degree Series with projects
Complete Data Visualization and Pre-processing Series with projects
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).
Subscribe today!
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 :
Part 2 of this series :
Here we go —
Pre-requisites
Before you start make sure you have a good grip on — Python, Pandas, Maths, Data preprocessing, Data visualization etc. The posts below cover all these in great detail —
Day 0:Complete Python And Projects — Mega Compilation
Day 1: Complete Data Preprocessing and Data Visualization with Projects — Mega Compilation Part 2
Day 2:Maths —
Statistics for Data Science and Machine Learning with Code Implementation
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.
Once you are done with pre-requisites then start from here —
Day 3: 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 3 post :
Day 4: 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 4 post :
Day 5: Project — DecisionTreeRegressor and RandomForestRegressor
In this post we developed an intuition and implemented DecisionTreeRegressor and RandomForestRegressor through a project.
Where to find Day 5 post :
Day 6: Project — Kaggle’s annual Machine Learning and Data Science Survey
In this post we covered second part of the Kaggle’s annual Machine Learning and Data Science Survey project.
Where to find Day 6 post :
Day 7: All the Important ML algorithms with projects
This post covered a quick overview of ML algorithms with projects.
Where to find Day 7 post :
Day 8: Machine Learning Classification and a Project
In this post we covered ML Classification in detail with a project.
Where to find Day 8 post :
Day 9: 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).
Where to find Day 9 post :
Day 10: 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).
Where to find Day 10 post :
Day 11: Machine Learning Clustering in detail with a project 1
In this post we covered Machine Learning Clustering in detail with a project( Part 1).
Where to find Day 11 post :
Day 12: Machine Learning Clustering in detail with a project 1
In this post we covered Machine Learning Clustering in detail with a project( Part 2).
Where to find Day 12 post :
Day 13: 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).
Where to find Day 13 post :
Day 14: 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).
Where to find Day 14 post :
Day 15: 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).
Where to find Day 15 post :
Day 16: Machine Learning Regression in detail with a project
In this post we covered univariate linear regression with a project.
Where to find Day 16 post :
Day 17: 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.
Where to find Day 17 post :
Day 18: Logistic regression with a project
In this post we covered logistic regression with a project.
Where to find Day 18 post :
Day 19: Logistic regression with another project
In this post we covered logistic regression with another project.
Where to find Day 19 post :
Day 20: Principal Component Analysis with a project
In this post we covered Principal Component Analysis with a project.
Where to find Day 20 post :
Part 2 of Data Science and ML : Coming soon!
Follow for more updates. Stay tuned and keep coding!
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
Hyperparameter Tuning with Keras Tuner
Custom Layers in Keras
