Day 1 —Data Science and ML Research (Papers) Simplified
One Research Paper at a Time…
Welcome back peeps! Hope all’s going well.
Well, I’m beyond excited to share that I’ll be starting my PhD in Computer Science soon. I have always been interested in pragmatic research and as much as I’m thrilled ( and a little nervous); I’m taking this opportunity to develop a new series ( along with two other series that you can find here )— Data Science and ML Research(papers) simplified where I’ll be covering and sharing what, why and how of the different Research papers in simple terms that I’ll be reading along the way.
Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) —
To put it straight, jumping ships from industry to the core practical research isn’t easy. Research is a different ball game altogether and like me, there must be 1000’s of existing/prospective PhD CS students who will/are already sailing on this ship and enjoying/exploring the thrill of Research. So, this series is for them and myself and If I get time out of my busy schedule, I might start a youtube channel dedicated to Data Science and ML Research.
I have an amazing team of mentors ( from both industry and academia who are always there to resolve any hiccups that I face and often tell me to go slow…haha) to advise me, help refine my research directions, navigate me in the right direction when I’m strayed and what not!
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
Tech Newsletter —
If you are interested, you can join my newsletter through which I send Research coverage, 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 :
Alright! Lets talk about the fields/papers we will be covering ( tentative list)—
Goal —
The goal is to develop good & practical understanding (depth) in each of these sub topics progressively and later develop an edge in one of the sub topic and further the existing research.
Source —
Source of the research papers — Google AI, FAIR, Papers with Code, ArXiv.org, Research Gate, AAAI and multiple ML conferences.
Areas —
Data Science Research Areas
Paper Focus —
- Data mining and learning
- Data Engineering
- MLOps
- Large-scale databases
- Information and knowledge retrieval and semantic search
- Learning for structured and relational data
- Data Dimensionality reduction
- Latent semantics
- Social/databases Query and Search
- Search and recommendation
- Large-scale recommender and search systems
- Prescriptive analytics and data visualization
- Knowledge discovery
Machine Learning Research Areas
Paper Focus —> NLP and ( Bit of ) Computer Vision
Natural Language Processing —
1.Machine Reading comprehension
2. Transfer learning and multi-task learning
3. Multi-modal learning
4. Text Classification and Summarization
5. Question Answering
6. Sentence Level semantics and Argument Mining
7. Sentence Similarity
8. Speech Recognition
9. Neural Machine Translation
10. Document Summarization
11. Textual Inference
Computer Vision —
- Augmented reality
- Pattern recognition
- Stochastic Models
You are welcome to provide inputs (especially a good recommendation for the research papers you deem important and that I should read and cover in this series) and amazing ( practical) ideas!
See you down the line with one research paper at a time ;)
I’m excited!! Are you?
Day 2 : Coming Soon!
All the Complete System Design Series Parts —
6. Networking, How Browsers work, Content Network Delivery ( CDN)
Github —
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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For Python Projects —
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
