learning
datacamp
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learning
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A Clear roadmap to complete learning AI/ML by the end of 2022 from ZERO
Not exactly a roadmap, but I documented my learning journey here: https://github.com/amitness/learning
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[D] How software engineer get into machine learning job role?
My general transition occurred as follows: 2017-2018: - Worked as full-stack web developer in Python + Angular. Worked with a stack that had some overlap with ML Engineering (Flask, AWS Services, SQL) - In the weekends and evenings, I started doing MOOCs + practicing tons of ML. The courses I follow are here. My focus was both to go broad first and then slowly dive deep.
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Resources for self-study NLP
Personally, I'm following this to get good at NLP: https://github.com/amitness/learning#be-able-to-implement-nlp-models
datacamp
What are some alternatives?
udacimak - Udacity Nanodegree and Course Downloader
AI-Hacktoberfest - Welcome to the Hacktoberfest Challenge for Artificial Intelligence / Machine Learning ! Today we will be assessing your skills to Predict Forest Fire Areas given its various parameters!
udacity-nanodegrees - :mortar_board: List of Udacity Nanodegree programs with links to the free courses in their curricula
lovely-tensors - Tensors, ready for human consumption
udacity-nanodegree-react - Study notes and projects from the Udacity's React Nanodegree program
data-science-notes - Notes of IBM Data Science Professional Certificate Courses on Coursera
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
pandas-profiling - Create HTML profiling reports from pandas DataFrame objects [Moved to: https://github.com/ydataai/pandas-profiling]
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Twitter-sentiment-analysis - A sentiment analysis model trained with Kaggle GPU on 1.6M examples, used to make inferences on 220k tweets about Messi and draw insights from their results.
DataScienceProjects
Introduction_to_statistical_learning_summary_python - Summary of each chapter of the book- Introduction of Statistical Learning (ISL), along with Python code & data.