Requirements

have a PC or mac. Must have desire to learn programming. HD monitor is preferred.

Description

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.

This is the definitely the most advanced yet simple Python fullstack course online. There is no other course ANYWHERE that goes as far into Data Science and Machine learning/ Artificial Intelligence as a stand alone topic, let alone with a FULLSTACK Python course preceding the data science. We can literally take someone with no programming experience and have them doing AI programs in about 2 weeks (or faster if they study daily). Whether you have never programmed before, already know basic syntax, or want to finally advance your skillset, this course is for you! In this course we will teach you HTML, CSS, Bootstrap, javascript, jQuery and Python 3.

With over 170 lectures and more than 30 hours of video this course is extremely comprehensive

We cover a wide variety of topics, including:

HTML
CSS
Bootstrap (to make responsive websites fast!)
javascript (to interact with users)
jQuery (to further interact with users using clicks and mouseovers)
Installing Python
Running Python Code
Strings
External Modules
Object Oriented Programming
Inheritance
Polymorphism
Lists
Dictionaries
Tuples
Sets
Number Data Types
Print Formatting
Functions
Scope
args/kwargs
Built-in Functions
Debugging and Error Handling
Modules
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
26. Spectral
27. PCA
28. SVD
29. Low Dimensional

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.
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.

Download Part 01
Download Part 02
Download Part 03