100 days : Your Data Science and ML Degree — Part 3
Everything you need to know…
Welcome back peeps. Hope all’s well at your end. While I’m extremely busy with work; I target to write once every three days ( mostly on weekends) if my schedule allows. This post is a 100 days — your Data Science and ML degree ( Part 3) — mega compilation of Data Science and ML theory and projects that we have built till now and progressing forward — what to expect.
Some of the other best Series —
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
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 :
You can use this as a starting point to advanced level and devise your own Data Science and ML degree ( PS: you don’t have to buy expensive courses to be able to learn/get into/ build data science and ML).
Part 1 ( Day 1- 71 ) of this series can be found here —
Part 2 ( Day 72—91 ) of this series can be found here —
Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) —
Lets dive in for Day 92 -100.
Complete Data Preprocessing and Data Visualization with Projects — Mega Compilation Part 2
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 :
Project — Detailed Analysis of the Netflix Content.
In this post we covered detailed Analysis of the Netflix Content.
Machine Learning Classification and a Project
In this post we covered ML Classification in detail with a project.
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).
Machine Learning Clustering in detail with a project 1
In this post we covered Machine Learning Clustering in detail with a project( Part 1).
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).
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).
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.
Logistic regression with a project
In this post we covered logistic regression with a project.
Logistic regression with another project
In this post we covered logistic regression with another project.
Advanced Regression Techniques with project ( Part 1)
In this post we covered Advanced Regression Techniques with a project
Advanced Regression Techniques with project ( Part 2)
In this post we covered Advanced Regression Techniques with a project
Support Vector Machine with a project
In this post we covered Support Vector Machine with a project
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
Multilayer Perceptron with project
In this project we implemented a multilayer Perceptron model with Keras.
Pytorch and ResNet with a project
In this post we learned about the basics of PyTorch ( one of my favorite library) and ResNet.
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.
30 days of Natural Language Processing Series with Projects
In this post we covered some more basics of NLP and then started with SpaCy.
30 days of Natural Language Processing Series with Projects
In this post we covered advanced concepts of SpaCy in detail.
30 days of Natural Language Processing Series with Projects
In this post we SpaCy with a project where we implemented the advanced concepts of SpaCy like Tokenization, POS tagging, Chunking, Named Entities Recognition ( NER)
30 days of Natural Language Processing Series with Projects
In this post we covered Regular Expressions — Part 1 in NLP. Regular Expressions are expressions/patterns used to find or match character combinations in text/strings. These are text-matching tool embedded in Python which are very useful in creating string searches/performing any modifications in Strings.
30 days of Natural Language Processing Series with Projects
In this post we covered Regular Expressions — Part 2 in NLP.
30 days of Natural Language Processing Series with Projects
In this post we covered Natural Language Tool Kit — Part 1 in detail. Natural Language Toolkit (NLTK ) is an amazing library for working in linguistics, natural language using Python which lets you analyze linguistic structure, classification, tokenization, stemming, tagging, parsing, and semantic reasoning, corpora, categorizing text etc.
30 days of Natural Language Processing Series with Projects
In this post we covered Pythonic code in detail that we will be using in NLP projects.
Part 4 of this series : Coming soon!
Follow for more updates. Stay tuned and keep coding!
More Projects —
Complete Python And Projects — Mega Compilation
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.
Follow for more updates, stay tuned and of-course let me end this post with a quote by Steve Jobs ;)
“Your time is limited, so don’t waste it living someone else’s life.”
