Machine Learning Workshops

Five Days to Unsupervised and Deep Learning

About the workshop

This is a five day workshop which will progressively take you through various Unsupervised and Deep Learning, providing hands on experience on problems based on Artificial Neural Networks, Convolution Neural Networks, Clustering, Feature Engineering and Optimization. At the end of this workshop, you would have solved and uploaded your results to the respective sites.



Day 01 : Unsupervised Learning - Clustering


Clustering is an unsupervised machine learning method. It is grouping of data points into groups of similar characterstics.Through this workshop, you will get to know how clustering is done and will get hands-on experience on KMeans algorithm. You will get to solve a problem which is based on clustering.


Day 02 : Introduction to Artificial Neural Networks


When we consider human brain, has a unique characteristic of creating transient states through neurons in between the sensory organs and the brain. These transient states create randomness in decision making. So, in artificial neural networks, transient states which allow machines to learn in a more sophisticated manner. In this session, you will get an introduction to artificial neural networks.


Day 03 : Building Of Artificial Neural Networks


This session of workshop deals with the building of Artificial Neural Networks.It delves you deeper into each step and associated concepts involved in building of artificial neural networks. The various ways in which the input layer, hidden layer and output layers can be added with a hands-on experience on a live problem.




Day 04 : Introduction to Convolution Neural Networks


Convolution Neural Networks are used mostly in classification of images into different classes based on the features of the images. It is most popularly used in the classification of different objects such as bottle, person, chair etc.This session of workshop provides introduction to CNN and various terms involved in it.




Day 05 : Building Of Convolution Neural Networks


This session of workshop deals with the building of Convolution Neural Networks. It delves you deeper into each step and associated concepts involved in building of Convolution Neural Networks. This workshop covers concepts like Flatening, Max Pooling and Convolution with hands-on experience on a live problem



Key takeaways

Recognizing the problem

A high level overview of what machine learning is and to recognize the problems that can be solved with Machine Learning.

Selecting the techniques

Select the right technique to solve the problem (is it a classification problem? a regression? needs preprocessing?).

Tools

A few libraries like Numpy, Pandas, Scikit-Learn, to start your learning experience, that you can improve upon.

Certificate

A certificate for succesful completion of the course.

Conclusion

Finally, you would be able to decide if Machine Learning is for you and what you would have to do next, if you want to make this your career.

Prerequisites

  • Having a good knowledge of programming concepts (writing loops, data structures, functions and recursive functions).
  • Knowledge of Python. (If you are not sure about your knowledge of python, sign up for the Basic Python for Data Science course).
  • Laptop with Wifi capability and Python 3.x installed.
  • A desire and thirst for knowledge.