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?
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
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
Train Test Splitting
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
Overfitting / Underfitting
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).
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