Mastering Machine Learning with Python





The Python ecosystem is growing and may become the dominant platform for machine learning. The primary rationale for adopting Python for machine learning is because it is a general purpose programming language that we can use both for R&D and in production. In this talk I will discuss 1. Python and its rising use for machine learning, 2. SciPy and the functionality it provides with NumPy, Matplotlib and Pandas. 3. scikit-learn for machine learning algorithms, TensorFlow and Keras for Deep learning and PyTorch for Natural Language Processing, 4. How to setup your Python ecosystem for machine learning and what versions to use. At the end I will also give case studies on using this Python ecosystem for biomedical applications.


This talk will be of general in nature. Those who are witnessing the recent AI hype should be able to follow my talk. Basic python knowledge is assumed.

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Speaker Info:

Parthiban Srinivasan is the CEO of VINGYANI, a data science company deals with Informatics 2.0, that is, Deep learning, Natural Language Processing and Machine Learning for Drug Discovery and Health. Parthiban Srinivasan is an experienced data scientist, earned his PhD from Indian Institute of Science, specializing in Computational Chemistry. He holds dual Masters Degree- one in Science and the other in Engineering. After his PhD, he continued the research at NASA Ames Research Center (USA) and Weizmann Institute of Science (Israel). Then he worked at AstraZeneca in the area of Computer Aided Drug Design for Tuberculosis. Later, he headed informatics business units in Jubilant Biosys and then in GvkBio before he floated the company, Parthys Reverse Informatics. Now his recent venture is VINGYANI.

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Section: Data science
Type: Talks
Target Audience: Beginner
Last Updated:

Target Audience:

Anyone who wish to get a birds eye view on Machine Learning and get a glimpse of various open source libraries available to implement those data science concepts. People who are familiar with the basic linear algebra mathematical notation and familiarity with Python will find this talk beneficial.

Objective: People will learn the step-by-step process that they can use to get started

and become good at machine learning for predictive modeling with the Python ecosystem.


This talk will have three main parts. And the given time will be distributed equally to all these three areas. 1/3 to Python Ecosystem, 1/3 to Common tasks in machine learning and 1/3 to Applications.

I. Python ecosystem for machine learning: SciPy and the functionality it provides with NumPy, Matplotlib and Pandas. Sci-kit Learn, TensorFlow and Keras.

II. Overview of common tasks in Machine Learning: 1. Define Problem 2. Analyze Data 3. Prepare Data 4. Evaluate Algorithms 5. Improve Results and

  1. Present Results

III. Applications: Showcase the examples in various areas, in particular, in the biomedical research.

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