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The Complete Collection of Data Science Projects – Part 2

The second part covers the list of Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Data Engineering, and MLOps.

The Complete Collection of Data Science Projects - Part 2
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Editor's note: For the full scope of repositories included in this 2 part series, please see The Complete Collection of Data Science Projects – Part 1.


Machine Learning


Machine learning is a hot topic in data science, and you will learn about the classification, regression, and clustering projects to solve business problems. It will help you understand the tabular dataset, data processing, training on algorithms, and model validation. 


Deep Learning


You will learn more advanced machine learning algorithms, neural networks, and data processing techniques. Deep learning is a huge subject, and to master it, you need to learn its applications in computer vision, NLP, forecasting, automatic speech recognition, generative art, and reinforcement learning. 

  • Reinforcement Learning: Tutorial
  • Gender and Age Detection with OpenCV: Tutorial
  • Deep Learning for Time Series Forecasting: Tutorial


Computer Vision


In computer vision, you learn to process image data and train the model for various computer vision tasks such as image classification, generation, segmentation, and object detection. 


Natural Language Processing (NLP)


You will learn to understand language through images, text, and audio. Due to the introduction of large language models and transformers NLP has seen multiple applications in the real world. It is used for translation, question and answers, text summarization, text classification, text generation, and conversational AI. 


Data Engineering


Design, validate, and deploy data pipelines for data science projects. You will learn everything about the data engineering process. You will also learn how these modern tools integrate to provide seamless data streams. It will introduce you to ETL, data modeling, orchestration, analytics, and serving tools. 




It is the production side of machine learning where engineers test, retrain, validate, and server inference in production. You will learn about ml pipeline tools, experiment and artifact tracking, storing and versioning data and models, cloud computing, REST API, and web applications. You will learn to create an end-to-end machine learning system. 




Working on projects and replicating the results will make you good at problem-solving, and it will also help you land a dream job. 

I will suggest beginners and people who are looking for jobs either start working on a pet project or contribute to open source projects to learn more about standard practices. 

We have learned about machine learning, deep learning, computer vision, natural language processing, data engineering, and MLOps. The projects consist of descriptions and code sources. Some of them even have a detailed tutorial to guide you throughout the project. 

In the previous part, we have covered:

  1. Programming
  2. Web scraping
  3. Data Analytics
  4. SQL
  5. Business Intelligence
  6. Time Series 

This is the 5th edition in the collection series, check out:

  1. The Complete Collection of Data Science Cheat Sheets – Part 1 and Part 2
  2. The Complete Collection of Data Repositories – Part 1 and Part 2
  3. The Complete Collection of Data Science Books – Part 1 and Part 2
  4. The Complete Collection of Data Science Interviews – Part 1 and Part 2

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.