HTML To Artificial Intelligence Deep Learning bootcamp Cornell University course w/Machine Learning! New for 2018!
What you’ll learn
- Students will be able to create websites, build applications, create Artificial Intelligent learning programs that can recognize handwriting and learn while analyzing data.
- Will help you get a job as a Fullstack programmer or Artificial Intelligence data scientist.
- Build over 10 AI data analysis tools
- have a PC or mac. Must have desire to learn programming. HD monitor is preferred.
My name is GP. I used AI to classify brain tumors. I have 11 publications on Pubmed talking about that. I went to Cornell and taught at UCSF, NIH, Cornell University and Amherst College.
We are offering LIVE HELP M-F 9-5 and also outside those hours when online.
This course will be continually updated and we answer all questions. We will continue updating content based on both user demand and changes in machine learning and AI. If you have taken a previous bootcamp but still are struggling, this course will fill in the holes and have you applying Python on lots of different projects. You will learn faster by
This is the only fullstack course that teaches you everything from basic frontend HTML to Python 3, Machine learning, Tensor Flow, and Artificial Intelligence / Recurrent Neural Networks!
This is a large course, but it is still easy! The secret to this course is that to learn rapidly, we present information in small steps, so that no one step seems difficult. Of course, there are lots of steps, so the knowledge builds fast, but its on a very strong foundation.
With over 170 lectures and more than 30 hours of video this course is extremely comprehensive
We cover a wide variety of topics, including:
- Bootstrap (to make responsive websites fast!)
- jQuery (to further interact with users using clicks and mouseovers)
- Installing Python
- Running Python Code
- External Modules
- Object Oriented Programming
- Number Data Types
- Print Formatting
- Built-in Functions
- Debugging and Error Handling
- File I/O
- Advanced Methods
- Decorators/ Advanced Decorators
- and much more!
For Data Science / Machine Learning / Artificial Intelligence
- –1. Machine Learning
- –2. Training Algorithm
- –3. SciKit
- –4. Data Preprocessing
- –5. Dimesionality Reduction
- –6. Hyperparemeter Optimization
- –7. Ensemble Learning
- –8. Sentiment Analysis
- – 9. Regression Analysis
- –10.Cluster Analysis
- –11. Artificial Neural Networks
- –12. TensorFlow
- –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
- –7. Correlations
- –11. KNN
- –12. Decision Tree
- –13. Random Forests
- –14. OLS
- –15. Evaluating Linear Model
- –16. Ridge Regression
- –17. LASSO Regression
- –18. Interpolation
- –19. Perceptron Basic
- –20. Training Neural Network
- –21. Regression Neural Network
- –22. Clustering
- –23. Evaluating Cluster Model
- –24. kMeans
- –25. Hierarchal
- –27. PCA
- –28. SVD
- –29. Low Dimensional
You will get lifetime access to over 180 lectures plus corresponding Notebooks for the lectures!
This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you’ll get your money back.
Learn Python and AI in the easiest possible way, so you can advance your career quickly and easily.
Who is the target audience?
- Beginners who have never programmed before.
- People who took a programming bootcamp but are looking to apply that knowledge to build something other than very basic projects.
- Intermediate Python programmers who want to understand Artificial Intelligence Programming.
Who this course is for:
- Anyone who wants to learn fullstack in Python 3 and apply it to making AI immediately. If you are a Python 3 Expert, you will still gain knowledge from the 45 projects.
- Python Developers who want to get started using Machine Learning in a realistic way using numerical or image data sets.
Created by GP Shan
Last updated 6/2019
Size: 14.78 GB