Data Science And Machine Learning Projects — Mega Compilation Part 4
Part 4 …
Welcome back peeps. This post( part 3) is all about Data Science and Machine Learning Projects that you can build to practically understand the concepts.
Some of the other best Series —
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).
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Pre-requisites
Python projects —
Part 1 of this mega series ( Day 0 — Day 20) can be found here —
Part 2 of this mega series ( Day 21 — Day 35) can be found here —
Part 3 of this mega series ( Day 36— Day 48) can be found here —
Part 4:Here we go —
Day 49: 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
Where to find Day 49 post :
Day 50: Recurrent Neural Network with a project
In this post we covered the basics of Recurrent Neural Network with a project
Where to find Day 50 post :
Day 51: Language Classification with a project
In this post we covered the basics of Multinomial Naive Bayes through a project.
Where to find Day 51 post :
Day 52 : One Simple Trick to Scrape Tabular Data using Python
Data scraping is the process of importing information from a website into a spreadsheet or local file on your system and it’s one of the most efficient ways to get data from the web. Many of you must be familiar with the Cheerio library or Python with Beautiful Soup to scrape the data. In this article, I’m going to teach one simple trick to scrape tabular data using Python and Pandas with just four lines of code.
Where to find Day 52 post :
Day 53: Multilayer Perceptron with project
In this project we implemented a multilayer Perceptron model with Keras.
Where to find Day 53 post :
Day 54 : Bidirectional Encoder Representations from Transformers ( BERT) with a project
In this post we learned how to fine tune BERT for text classification.
Where to find Day 54 post :
Day 55 : Yellowbrick with 2nd project
In this project we implemented visualization using yellowbrick through a project
Where to find Day 55 post :
Day 56 : Yellowbrick with 3rd project
In this project we implemented visualization using yellowbrick through a project
Where to find Day 56 post :
Day 57 : 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.
Where to find Day 57 post :
Day 58 : 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
Where to find Day 58 post :
Day 59 : Deep learning and BERT
In this post we learned how to perform sentiment analysis using BERT.
Where to find Day 59 post :
Day 60: RNN and LSTM through a project
In this post we covered the basics of RNN and LSTM through a project.
Where to find Day 60 post :
Day 61: Natural Language Processing and Convolutions
In this post we learned and implemented 1D Convolutions as Feature Extractors for Text in NLP.
Where to find Day 61 post :
Day 62 : 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.
Where to find Day 62 post :
More Projects —
Complete Python And Projects — Mega Compilation
Complete Data Preprocessing and Data Visualization with Projects — Mega Compilation Part 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.
Part 5 of this series : Coming soon!
For other projects, tune to —
Build Machine Learning Pipelines( With Code)
Recurrent Neural Network with Keras
Custom Layers in Keras
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.”
