Artificial Intelligence Bootcamp 44 projects Ivy League pro
What you'll learn
- Code for image recognition, handwriting recognition, data analysis, and create recurrent neural networks.
- Some experience with Python is needed. Statistics would be helpful but not required.
My name is Gopal. I used AI to classify brain tumors. I have 11 publications on pubmed talking about that. I went to Cornell University and taught at Cornell, Amherst and UCSF. I worked at UCSF and NIH.
AI and Data Science are taking over the world! Well sort of, and not exactly yet. This is the perfect time to hone you skills in AI, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as the dominant language in intelligence and machine learning programming because of its simplicity and flexibility, in addition to its great support for open source libraries and TensorFlow.
This video course is built for those with a NO understanding of artificial intelligence or Calculus and linear Algebra. We will introduce you to advanced artificial intelligence projects and techniques that are valuable for engineering, biological research, chemical research, financial, business, social, analytic, marketing (KPI), and so many more industries. Knowing how to analyze data will optimize your time and your money. There is no field where having an understanding of AI will be a disadvantage. AI really is the future.
We have many projects, such natural language processing , handwriting recognition, interpolation, compression, bayesian analysis, hyperplanes (and other linear algebra concepts). ALL THE CODE IS INCLUDED AND EASY TO EXECUTE. You can type along or just execute code in Jupyter if you are pressed for time and would like to have the satisfaction of having the course hold your hand.
I use the AI I created in this course to trade stock. You can use AI to do whatever you want. These are the projects which we cover.
For Data Science / Machine Learning / Artificial Intelligence
1. Machine Learning
2. Training Algorithm
4. Data Preprocessing
5. Dimesionality Reduction
6. Hyperparemeter Optimization
7. Ensemble Learning
8. Sentiment Analysis
9. Regression Analysis
11. Artificial Neural Networks
13. TensorFlow Workshop
14. Convolutional Neural Networks
15. Recurrent Neural Networks
Traditional statistics and Machine Learning
1. Descriptive Statistics
2.Classical Inference Proportions
3. Classical InferenceMeans
4. Bayesian Analysis
5. Bayesian Inference Proportions
6. Bayesian Inference Means
12. Decision Tree
13. Random Forests
15. Evaluating Linear Model
16. Ridge Regression
17. LASSO Regression
19. Perceptron Basic
20. Training Neural Network
21. Regression Neural Network
23. Evaluating Cluster Model
25. Hierarchal 26. Spectral
29. Low Dimensional
Who this course is for:
- Beginning to Pro Python Developers who want to get started using Machine Learning in a realistic way using numerical or image data sets.
BA Degree from Computer Science and Neuroscience from Cornell University in Ithaca NY
MD from Medical College of Ohio (now University of Toledo College of Medicine)
Statistics Certificate from University of California San Francisco
Worked at UCSF as Data Science Programmer from 2008-2012
National Institute of Health (NIH) as Data Science Programmer 2013-2018
NIH as Machine Learning Instructor 2016-2018
Nextcore AI data contracting for NIH 2016-present