100 days : Your Data Science and ML Degree — Part 4
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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 4) — mega compilation of Data Science and ML theory and projects that we have built till now and progressing forward — what to expect. 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).
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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Part 1 ( Day 1- 71 ) of this series can be found here —
Part 2 ( Day 72–91 ) of this series can be found here —
Part 3( Day 92–98 ) of this series can be found here —
Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) —
In this part, we will cover the projects that you can build once you get a leg up on the theory.
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.
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 :
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 :
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 :
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
RNN and LSTM with a project
In this post we covered the basics of RNN and LSTM with a project
Recurrent Neural Network with a project
In this post we covered the basics of Recurrent Neural Network with a project
Language Classification with a project
In this post we covered the basics of Multinomial Naive Bayes through a project.
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.
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
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
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)
Part 5 : Coming Soon!
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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.”
