erinkhoo / hard-skill-learning-list-2019

Learning topics with resources for 2019. Heavy focus on Machine Learning, Mathematics and supportive Software Engineering.

hard skill learning list 2019-2020

My learning list with resources for 2019 and 2020

Heavy focus on Machine Learning, Mathematics and supportive Software Engineering. I started this repo to prepare for a Masters in Artificial Intelligence with the University of Limerick. I have since been accepted and completed the MSc. over the last 24+ months.

Recent progress: (Sept 2020)

• top 9% in Kaggle Titanic: Machine Learning from Disaster
• top 34% in Kaggle Abstract and Reasoning Challenge
• top 13% in Kaggle Tweet Sentiment Extraction

Kaggle Profile Page

I have a number of example implementations in this Github folder

Feel free to follow me on Twitter

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SQL

• DataCamp - https://www.datacamp.com/courses/intro-to-sql-for-data-science

Python

• codecademy - https://www.codecademy.com/learn/learn-python-3
• Udacity - https://www.udacity.com/course/introduction-to-python--ud1110

Machine Learning

• fast.ai - https://course.fast.ai/index.html
• Coursera - https://www.coursera.org/learn/machine-learning
• Data Science Handbook - https://jakevdp.github.io/PythonDataScienceHandbook/
• Google ML Crashcourse - https://developers.google.com/machine-learning/crash-course/ml-intro
• Amazon Educate - https://aws.amazon.com/machine-learning/

Platforms

• Kaggle - https://www.kaggle.com/
• Google Colabortory - https://colab.research.google.com/
• Amazon Sage Maker - https://courses.edx.org/courses/course-v1:AWS+OTP-AWSD4+3T2018/course/
• Jupyter notebook examples - https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

Machine Learning frameworks

• MXnet - https://beta.mxnet.io/guide/index.html
• PyTorch - https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html
• Keras - https://keras.io/getting_started/intro_to_keras_for_engineers/
• XGBoost - https://xgboost.readthedocs.io/en/latest/
• CatBoost - https://catboost.ai/docs/

Mathematics

• Feynman Lectures; Algebra - http://www.feynmanlectures.caltech.edu/I_22.html
• Maths for ML - https://github.com/erinkhoo/Microsoft-DAT256x
• Statisitcs - http://onlinestatbook.com/
• Probability - https://www.youtube.com/playlist?list=PLwJRxp3blEvZ8AKMXOy0fc0cqT61GsKCG [Ben Lambert]
• r/learnmath - https://www.reddit.com/r/learnmath/comments/8p922p/list_of_websites_ebooks_downloads_etc_for_mobile/
• Linear Algebra - http://vmls-book.stanford.edu/
• Essence Linear Algebra [videos] - https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
• MIT Linear Algebra - https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
• Matrices - http://math.mit.edu/~gs/linearalgebra/linearalgebra5_1-3.pdf
• Matricies [videos] - https://www.youtube.com/watch?v=0oGJTQCy4cQ&list=PLi5giWKc4eO1G8oX3ft8ZuLQr4Y4idgng
• Calculus - https://brilliant.org/calculus/
• Bayesian Inference - https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb