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In this course, you will :
- Learn about various ML methods, Deep Learning, and their limitations, as well as how to drive accuracy and use the best training data for your algorithms.
- Investigate GANs and VAEs, then use your newfound knowledge to engage with AutoML to begin building algorithms that work for you.
- Exclusive interviews with industry leaders who manage Big Data for companies like McDonald's and Visa are available.
- By the end of this course, you will have learned how to code in various ways, such as using no-code tools, understanding Deep Learning, measuring and reviewing errors in your algorithms, and using Big Data to not only maintain customer privacy but also to develop different strategies that will drive your business
Syllabus :
1. Big Data and Artificial Intelligence
- Big Data Overview
- Big Data Analysis
- Data Management Tools
- Data Management Infrastructure
- Data Analysis: Extracting Intelligence from Big Data
- Introduction to Artificial Intelligence
- Machine Learning Overview
- Reinforcement Learning
- A Detailed View of Machine Learning
2. Training and Evaluating Machine Learning Algorithms
- Specific Machine Learning Methods: A Deep Dive
- Intro to Model Selection
- Feature Engineering and Deep Learning Introduction
- Deep Learning
- How Deep Learning Works
- Limitations of Deep Learning
- Evaluating ML Performance
- Common Loss Functions
- Tradeoffs Between Loss Functions
- How is Training Data Acquired?
- The Over-Fitting Problem
- Test Data
3. ML Application and Emerging Methods
- Natural Language Processing
- GANs and VAEs
- Intro to AutoML
- Using AutoML
- Teachable Machine
- TensorFlow Playground
- ML Operations
- Chicken and Egg
4. Industry Interview
- Interview With Ed Lee
