Python for Data Science


Professional instructors will teach you how to use python effectively. This workshop explores Python's place in the scientific ecosystem, and how the language, with several readily available open-source libraries, can serve as a powerful tool for data analysis.

What are the Topics Covered?

  • Python

    • Basics: Variables and Elementary Types, Operations, Console and Functions

    • Data Structures: Tuples, Lists, Sets, Dictionaries/Maps

    • Control flow statements: if, for, break, continue and else statements, while loops

  • Pandas:

    • Get started with data analysis tools in the pandas library

    • Use flexible tools to load, clean, transform, merge, and reshape data

  • Matplotlib & Seaborn

    • Create informative visualizations with matplotlib

    • Apply the pandas groupby facility to slice, dice, and summarize datasets

    • Analyze and manipulate regular and irregular time series data



Introduction to Machine Learning


These machine learning workshop present the basics behind the application of modern machine learning algorithms. We will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of over-fitting/under-fitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data.

  • Machine Learning Concepts

    • MLA’s Seven Step Supervised Learning Process

    • Data Abstraction

    • Feature Engineering

    • Train Test Splitting

    • Model Evaluations

  • Algorithms covered:

    • Logistic Regression

    • Linear Regression

    • Text / Sentiment Analysis

    • K - Means



Advanced Machine Learning & Deep Learning


Why Should I Learn Machine Learning & Deep Learning?

If you want to break into cutting-edge AI, this course will help you do so. Machine learning and Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Machine learning and Deep Learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.

Week 1 : Advanced Machine Learning. This week you will learn the math and the programming and some more algorithms

  • Machine Learning Concepts

    • Model Tuning

    • Feature Selections

    • Regularization

    • Overfitting / Underfitting

  • Algorithms covered:

    • XG-Boost

    • Time Series Analysis

    • K - Nearest Neighbours

    • and many more


Week 2 : Basic Deep Learning

Neural Networks has shown its power in Image Recognition, Character Recognition, Forecasting and many other areas where predictions can be made with a much higher accuracy than others. So the next four sessions aim at delving you deeper into the world of Deep Learning. You will know about Artificial Neural Networks (ANN) and Convolution Neural Networks (CNN) and have hands-on experience in building an ANN and CNN.

  • Session 1: Introduction to Artificial Neural Networks (ANN)

  • Session 2: Building of Artificial Neural Networks (ANN)

  • Session 3: Introduction to Convolution Neural Networks (CNN)

  • Session 4: Building of Convolution Neural Networks (CNN)

Week 3 : Advanced Deep Learning

After learning ANN and CNNs it is time to move on to advanced Neural Networks concepts. We go into Recurrent Neural Networks.

  • Session 1: Introduction to Recurrent neural network and LSTM

  • Session 2: Project in LSTM

  • Session 3: Autoencoders - Unsupervised Deep Learning

  • Session 4: GANs - Generate images

Who Can Attend?
People who have attended the Basics - Machine Learning 101 or has prior knowledge of Machine Learning (Python is preferred, but any programming background is fine).



Capstone Project


Participants will tackle real world issues and apply machine learning concepts in order to solve them in this three month long Capstone Project, that can show case their knowledge to prospective employers