Lists of all AI related learning materials and practical tools to get started with AI apps
Below is the learning material that will help you learn about Google Cloud.
Network
- Networking 101 – 43 mins
The codelab provides common cloud developer experience as follows:
- Set up your lab environment and learn how to work with your GCP environment.
- Use of common open source tools to explore your network around the world.
- Deploy a common use case: use of HTTP Load Balancing and Managed Instance Groups to host a scalable, multi-region web server.
- Testing and monitoring your network and instances.
- Cleanup.
Developing Solutions for Google Cloud Platform – 8 hours
Infrastructure
- Build a Slack Bot with Node.js on Kubernotes – 43 mins
- Creating a Virtual Machine – 10 mins
- Getting Started with App Engine (Python) – 13 mins
Data
- Introduction to Google Cloud Data Prep – 7 mins
- Create a Managed MySQL database with Cloud SQL – 19 mins
- Upload Objects to Cloud Storage – 11 mins
AI, Big Data & Machine Learning
- Introduction to Google Cloud Machine Learning – 1 hour
- Machine Learning APIs by Example – 30 min
- Google Cloud Platform Big Data and Machine Learning Fundamentals
Additional AI Materials
- Auto-awesome: Advanced Data Science on Google Cloud Platform – 45 min
- Run a Big Data Text Processing Pipeline in Cloud Dataflow – 21 min
- Image Classification Using Cloud ML Engine & Datalab – 58 min
- Structured Data Regression Using Cloud ML Engine & Datalab – 58 min
(Optional) Deep Learning & Tensorflow
Additional Reference Material
- Big Data & Machine Learning @ Google Cloud Next '17 - A collection of 49 videos
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Source Berkeley
| Lecture Title | Lecturer | Semester | |
| Lecture 1 | Introduction | Dan Klein | Fall 2012 |
| Lecture 2 | Uninformed Search | Dan Klein | Fall 2012 |
| Lecture 3 | Informed Search | Dan Klein | Fall 2012 |
| Lecture 4 | Constraint Satisfaction Problems I | Dan Klein | Fall 2012 |
| Lecture 5 | Constraint Satisfaction Problems II | Dan Klein | Fall 2012 |
| Lecture 6 | Adversarial Search | Dan Klein | Fall 2012 |
| Lecture 7 | Expectimax and Utilities | Dan Klein | Fall 2012 |
| Lecture 8 | Markov Decision Processes I | Dan Klein | Fall 2012 |
| Lecture 9 | Markov Decision Processes II | Dan Klein | Fall 2012 |
| Lecture 10 | Reinforcement Learning I | Dan Klein | Fall 2012 |
| Lecture 11 | Reinforcement Learning II | Dan Klein | Fall 2012 |
| Lecture 12 | Probability | Pieter Abbeel | Spring 2014 |
| Lecture 13 | Markov Models | Pieter Abbeel | Spring 2014 |
| Lecture 14 | Hidden Markov Models | Dan Klein | Fall 2013 |
| Lecture 15 | Applications of HMMs / Speech | Pieter Abbeel | Spring 2014 |
| Lecture 16 | Bayes' Nets: Representation | Pieter Abbeel | Spring 2014 |
| Lecture 17 | Bayes' Nets: Independence | Pieter Abbeel | Spring 2014 |
| Lecture 18 | Bayes' Nets: Inference | Pieter Abbeel | Spring 2014 |
| Lecture 19 | Bayes' Nets: Sampling | Pieter Abbeel | Fall 2013 |
| Lecture 20 | Decision Diagrams / Value of Perfect Information | Pieter Abbeel | Spring 2014 |
| Lecture 21 | Machine Learning: Naive Bayes | Nicholas Hay | Spring 2014 |
| Lecture 22 | Machine Learning: Perceptrons | Pieter Abbeel | Spring 2014 |
| Lecture 23 | Machine Learning: Kernels and Clustering | Pieter Abbeel | Spring 2014 |
| Lecture 24 | Advanced Applications: NLP, Games, and Robotic Cars | Pieter Abbeel | Spring 2014 |
| Lecture 25 | Advanced Applications: Computer Vision and Robotics | Pieter Abbeel | Spring 2014 |
Additionally, there are additional Step-By-Step videos which supplement the lecture's materials. These videos are listed below:
| Lecture Title | Lecturer | Notes | |
| SBS-1 | DFS and BFS | Pieter Abbeel | Lec: Uninformed Search |
| SBS-2 | A* Search | Pieter Abbeel | Lec: Informed Search |
| SBS-3 | Alpha-Beta Pruning | Pieter Abbeel | Lec: Adversarial Search |
| SBS-4 | D-Separation | Pieter Abbeel | Lec: Bayes' Nets: Independence |
| SBS-5 | Elimination of One Variable | Pieter Abbeel | Lec: Bayes' Nets: Inference |
| SBS-6 | Variable Elimination | Pieter Abbeel | Lec: Bayes' Nets: Inference |
| SBS-7 | Sampling | Pieter Abbeel | Lec: Bayes' Nets: Sampling |
| SBS-8 | Maximum Likelihood | Pieter Abbeel | Lec: Machine Learning: Naive Bayes |
| SBS-9 | Laplace Smoothing | Pieter Abbeel | Lec: Machine Learning: Naive Bayes |
| SBS-10 | Perceptrons | Pieter Abbeel | Lec: Machine Learning: Perceptrons |
Source:https://medium.com/intuitionmachine/the-deep-learning-roadmap-f0b4cac7009a
Source: http://www.asimovinstitute.org/neural-network-zoo/
Source: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet
Source: http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
Source: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
Source: https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/
Source: http://datasciencefree.com/python.pdf
Source: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA
Source: https://www.dataquest.io/blog/numpy-cheat-sheet/
Source: http://datasciencefree.com/numpy.pdf
Source: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE
Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb
Source: http://datasciencefree.com/pandas.pdf
Source: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U
Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb
Source: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet
Source: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk
Source: http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html
Source: https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb
Source: https://github.com/bfortuner/pytorch-cheatsheet
Source: http://www.wzchen.com/s/probability_cheatsheet.pdf
Source: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf
Source: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf
Source: http://tutorial.math.lamar.edu/getfile.aspx?file=B,41,N
