If you would like to understand more about what is Artificial Intelligence, Machine Learning, and how to apply this in your code. I suggest you to follow this video series from Cassie Kozyrkov, a very well known Data Decision Intelligence expert in Google.
1st Video – Introduction to Machine Learning & Artificial Intelligence :
2nd Video – Life of an Artificial Intelligence Project :
3rd Video – Taking your AI project from Prototype to Production :
4th Video – Guide to Artificial Intelligence Algorithms :
I personally really learnt a lot from her videos, especially on how to explain ML to public.
To watch the videos in byte size, based on the topic that you preferred. Here are the list:
Basic Concepts
- Basics: MFML 000 – Welcome
- Basics: MFML 001 – What is machine learning?
- Basics: MFML 002 – Why use machine learning?
- Basics: MFML 003 – How does machine learning work?
- Basics: MFML 004 – How to test ML
- Basics: MFML 005 – What’s inside the black box?
- Basics: MFML 006 – Simple linear regression
- Basics: MFML 007 – Multiple linear regression
- Basics: MFML 008 – Feature engineering
- Basics: MFML 009 – What is AI?
- Basics: MFML 010 – Why did we wait so long for AI?
- Basics: MFML 011 – Geoff, Fei-Fei, and Jeff
- Basics: MFML 012 – Real applications
- Basics: MFML 013 – How to find good AI use cases
- Basics: MFML 014 – Human creativity in AI
- Basics: MFML 015 – How Do GANs work?
- Basics: MFML 016 – Why trust AI?
- Basics: MFML 017 – Explainability and AI
- Basics: MFML 018 – Intro to training, validation, and testing
- Basics: MFML 019 – How to avoid machine learning pitfalls
- Basics: MFML 020 – Decision Intelligence
- Basics: MFML 021 – Why do businesses fail at machine learning?
- Basics: MFML 022 – Skilled decision-makers
- Basics: MFML 023 – Reliable or unreliable?
- Basics: MFML 024 – Preventable disasters
- Basics: MFML 025 – Wish responsibly
- Basics: MFML 026 – AI is a team sport!
- Basics: MFML 027 – Our AI future
The 12 Steps of AI Step 0: Reality check
- Step 0: MFML 028 – The 12 steps of AI
- Step 0: MFML 029 – Where to start with applied AI?Â
- Step 0: MFML 030 – Classification vs regression
- Step 0: MFML 031 – Instances, features, and targets
- Step 0: MFML 032 – Supervised learning
- Step 0: MFML 033 – Unsupervised learning
- Step 0: MFML 034 – Semi-supervised learning
- Step 0: MFML 035 – Reinforcement learning
- Step 0: MFML 036 – What on earth is data science?
- Step 0: MFML 037 – Data science flowchart
- Step 0: MFML 038 – Don’t forget data!
Step 1: Define your objectives
- Step 1: MFML 039 – What is “good behavior” for AI?
- Step 1: MFML 040 – False positives and true negatives
- Step 1: MFML 041 – Confusion matrix
Step 1: MFML 042 – Performance metrics - Step 1: MFML 043 – Ground truth
- Step 1: MFML 044 – Precision vs recall
- Step 1: MFML 045 – What is optimization?
- Step 1: MFML 046 – Loss functions
- Step 1: MFML 047 – Setting launch criteria
Step 2: Get access to data
- Step 2: MFML 048 – Data engineering
Step 3: Split your data
- Step 3: MFML 049 – The danger of overfitting
- Step 3: MFML 050 – Should you care about underfitting?
- Step 3: MFML 051 – The importance of data splitting
Step 4: Explore your data
Step 5: Prepare your tools
- Step 5: MFML 053 – How to select an AI algorithm
- Step 5: MFML Part 4 – Guide to AI algorithms (not bite sized!)
Step 6: Use your tools to train some models
- Step 6: MFML 054 – Is training an AI system easy?
- Step 6: MFML 055 – A dataset’s idea shape
- Step 6: MFML 056 – How to speed up your ML/AI training phase
- Step 6: MFML 057 – Statistics versus “statistics”
- Step 6: MFML 058 – When your machine learning project takes forever
- Step 6: MFML 059 – Regularization
- Step 6: MFML 060 – Features you should never use in AI
- Step 6: MFML 061 – Can you skip the training phase in AI?
Step 7: Debug, analyze, and tune
- Step 7: MFML 062 – Debugging your machine learning model
- Step 7: MFML 063 – Hyperparameter tuning
- Step 7: MFML 064 – What is a holdout set and how do you use it?
- Step 7: MFML 065 – Understanding k-fold cross-validation
- Step 7: MFML 066 – Advanced AI debugging
- Step 7: MFML 067 – What if you skip debugging?
Step 8: Validate your models
- Step 8: MFML 068 – What to do when model validation fails
- Step 8: MFML 069 – Model validation done right
- Step 8: MFML 070 – Validation roulette
Step 9: Test your model
- Step 9: MFML 071 – What’s the difference between testing and validation
- Step 9: MFML 072 – The 12 steps of statistics
- Step 9: MFML 073 – Interpreting AI test output
- Step 9: MFML 074 – Understanding p-values
- Step 9: MFML 075 – Statistical significance
- Step 9: MFML 076 – What should you do if testing fails
- Step 9: MFML 077 – The importance of testing
Step 10: Productionize your system
- Step 10: MFML 078 – Productionization
- Step 10: MFML 079 – Repurposing data safely
- Step 10: MFML 080 – Solving AI latency problems
- Step 10: MFML 081 – How often should you retrain your AI system?
- Step 10: MFML 082 – The training-serving skew
- Step 10: MFML 083 – Be careful with chained models
- Step 10: MFML 084 – Making tiny changes to AI code
- Step 10: MFML 085 – When your AI model fails retesting
- Step 10: MFML 086 – The danger of the long tail in AI
- Step 10: MFML 087 – How to catch outliers and AI failures
- Step 10: MFML 088 – AI safety and policy layers
Step 11: Run live experiments to launch safely
- Step 11: MFML 089 – Live traffic experiments
Step 12: Monitor and maintain… forever
- Step 12: MFML 091 – AI system maintenance
- Step 12: MFML 090 – Monitoring your AI system
Enjoy!
Kind Regards,
Doddi Priyambodo