Python AI and Machine Learning for Production & Development
- Basic Knowledge of Python
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:
Machine Learning - Giving Computers the Ability to Learn from Data
Training Machine Learning Algorithms for Classification
A Tour of Machine Learning Classifiers Using Scikit-Learn
Building Good Training Sets – Data Pre-Processing
Compressing Data via Dimensionality Reduction
Learning Best Practices for Model Evaluation & Hyperparameter Optimization
Combining Different Models for Ensemble Learning
Applying Machine Learning to Sentiment Analysis
Embedding a Machine Learning Model into a Web Application
Predicting Continuous Target Variables with Regression Analysis
Working with Unlabeled Data – Clustering Analysis
Implementing a Multi-layer Artificial Neural Network from Scratch
Parallelizing Neural Network Training with TensorFlow
Going Deeper: The Mechanics of TensorFlow
Classifying Images with Deep Convolutional Neural Networks
Modeling Sequential Data Using Recurrent Neural Networks
In addition to the preinstalled setup and demos, the VM also comes with:
Jupyter notebook for web based interactive development
JupyterHub for multiuser notebook environment to allow multiple users to simultaneously do development
Visual studio code IDE
The VM is available on :
Google Cloud Platform
- Python developers who are intrested in learning Artificial Intelligence and Machine Learning
- Creating and Connecting VM on Google Cloud Platform (GCP)11:12
- Creating and Connecting VM on Amazon Web Services (AWS)14:00
- Creating and Connecting VM on Microsoft Azure15:51
- How to run code examples in ipython notebook26:50
- How to use JupyterHub15:18
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