Python AI and Machine Learning for Production & Development

Learn AI & ML using demos

What you'll learn:

• Developing & deploying AI & Machine Learning applications using python AI & ML frameworks
• how to use most popular AI & ML frameworks: NumPy ,SciPy, Scikit-Learn, Matplotlib
• How to use Jupyter/iPython notebook for interactive development
• How to create multi-user notebook enviroment using JupyterHub

When you want to learn a new technology for professional use, there are two mutually exclusive options, either you learn it yourself or you go for instructor based training.

Self learning is least expensive but lot of time results in wasting time in finding right contents, setting up the environment , troubleshooting issues and may make you give up in the middle.

Instructor based training can be expensive at times and need your time commitment.

This course combines the best of both these options. The course is based on one of the most famous books in the field "Python Machine Learning (2nd Ed.)" by Sebastian Raschka and Vahid Mirjalili and provides you video tutorials on how to understand the AI/ML concepts from the books by providing out of box virtual machine with demo examples for each chapter in the book and complete preinstalled setup to execute the code.

You learn the concepts by self learning and get hands on executing the sample code in the virtual machine.

The demo covers following concepts:

1. Machine Learning - Giving Computers the Ability to Learn from Data

2. Training Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using Scikit-Learn

4. Building Good Training Sets – Data Pre-Processing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation & Hyperparameter Optimization

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multi-layer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper: The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

In addition to the preinstalled setup and demos, the VM also comes with:

1. Jupyter notebook for web based interactive development

2. JupyterHub for multiuser notebook environment to allow multiple users to simultaneously do development

3. Remote desktop

4. Visual studio code IDE

5. Fish Shell

The VM is available on :

1. Google Cloud Platform

2. AWS

3. Microsoft Azure