amai-gmbh / ai-expert-roadmap Goto Github PK

Consider adding the website url https://i.am.ai/roadmap/ to repo description, that would save lazy people some energy. :)

Hello! They could recommend some books that see the theoretical part. For now, I would be interested in knowing something related to the part of ¨Data Scientist (Statistics and visualitation)¨ and ¨Fundamentals (Exploratory Data Analysis /
Data Munging / - Wranglin)¨. Thanks in advance!

This is amazing. Thank you so much for creating it, and for presenting it in an easy-to-read format. It would be really cool if you guys put together an entire course, with videos and everything, teaching all these concepts. Just a suggestion. Thank you again!

Hey! I noticed you put logistic regression under regression. Despite its name, Logistic Regression is actually used for classification.

Thank you for piecing this all together. However, the roadmap is misleading in that there is a strict ordering of [data scientist -> machine learning scientist -> deep learning researcher]. This is misleading as it implies that data scientists are somehow of lower seniority in skills and experience than machine learning scientists and deep learning researchers. When really data scientist, ML scientist, and DL researchers share similar skills as well as having their very own unique skills that other roles might not have. For example, data scientists might be more business-oriented, ML/applied scientists might work closely on applied research/engineering to make sure the developed ML models go into production, and DL researchers might work more on the pure research side. In no way is one job on higher seniority than the other in terms of skills, years of experience, and even education level (although DL researchers typically have PhD more often than ML scientists and data scientists).

Hi all, this is a very nice chart but I believe that there should be slight modifications.

Data Science Roadmap

"Dimensionality and Numerosity Reduction" is a large topic which would include the study of the "Principal Component Analysis" (PCA) algorithm as the very first thing you'd do. Yet PCA is seen as being the very final thing you look at in this section. I think you should put PCA right before Dimensionality Reduction or you should combine them.

In the "Visualization section" you list some nice plotting libraries, but I think that Bokeh should be included here because it is a superior python plotting library (out of the box GPU/OpenGL support allowing for plotting millions of points, significantly more flexible interaction system) combined to the other options and is quickly becoming one of the important graphing libraries.

Under "Data Sources" you may want to put "Data Mining and Web Scraping" or something along those lines since I think a Data Scientist should be able to get their own data rather than go on Kaggle or github awesome pages.

Machine Learning Roadmap

Subsections under "Association Rule Learning" should be "Apriori algorithm", "ECLAT algorithm" and "Fp-trees"

Subsections under Dimensionality Reduction should include (after PCA): "Random Projection", "NMF", "T-SNE", "UMAP"

Subsections under Clustering should include (after Agglomerative): "OPTICS" and "HDBSCAN"

Subsections under "Classification" should include "Guassian Mixture Models"

Logistic Regression is actually a binary classification algorithm, despite its name, so move it from regression to Classification

Moving Huggingface Transformers out from here and into the Deep Learning section.

Deep Learning Roadmap

Add a new section under "Architectures" called "Attention Mechanisms/Transformers"
Add a new section under Architectures called "NEAT/Evolving Architectures"

Big Data Engineer

Add a new blue section under "Tools" for Dask, Numba, Onnx, and OpenVino

If it's really easy to generate these plots, I'm willing to make these changes and submit a PR. What are your thoughts on implementing some or all of these changes?

Why isn’t bootstrapping included in confidence intervals? It is a very useful and popular tool.

Hi,

I am trying to apply AI and ML algorithms in my data, there are hundred thousand stores with competitors, where gas prices changes 15 minutes once.
Assume in each city if I have my own gas station and how should I give my prices so that more customers come to my store.

Can we apply AI for my above logic

Thanks

We could improve the Data Engineer Roadmap based on free resources of https://awesomedataengineering.com/.

This is my first step in exploring GitHub

Tyro at github
So creating an issue for learning purpose

the leadpop application that a website in your mobile

The Medium story linked to Denoising is deleted.

https://medium.com/analytics-vidhya/dealing-with-noisy-data-in-data-science-e177a4e32621

e.g. https://medium.com/@srinivaspadmanabhuni/10-tests-for-your-ai-ml-dl-model-40168a41e9ad

The link to pandas homepage return a "Page not found error"

Quick question, is there a print friendly version?

We should develop agents that deliver goods for the Covid-19 patients in India since we are suffering!

I have a doubt that is there a way to do machine learning and data science with R programming language instead of Python