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 a lot of time results in wasting time in finding the 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 Edition)" 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 set up to execute the code.

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

What will you learn in this course?

• 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 and 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 virtual machine also comes with:

• Jupyter notebook for web-based interactive development
• JupyterHub for multiuser notebook environment to allow multiple users to do development simultaneously
• Remote desktop
• Visual studio code IDE
• Fish Shell

The virtual machine is available on:

• Google Cloud platform
• AWS
• Microsoft Azure