Past Meetup

DataGiri's Code-along Saturdays

This Meetup is past

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Details

Welcome to DataGiri's Code-Along Saturdays workshop. This is your opportunity to learn hands-on a wide variety of data science skills at this 8-hour workshop. In addition, to growing your skill set, you also get to network with peers and industry professionals.

The event is FREE. Please note that you are required to bring along your own laptops to participate in the workshop. Your laptop must have Anaconda 3.6 pre-installed before you begin the workshop. Find the software here: www.anaconda.com/download/#macos.

REGISTER HERE : https://goo.gl/3ynPJ9

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*Agenda:

10:00 AM - 12:00 PM : Introductory Statistics for Machine Learning

12:00 PM - 2:00 PM : Data Pre-processing and EDA

2:00 PM - 4:00 PM : Logistic Regression

4:00 PM - 6:00 PM : Decision Trees

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* Session #1:

Topic: Introductory Statistics for Machine Learning

Instructor : Kalpesh Patil

Learning Outcomes : Students will be able to understand the theory and mathematics required to apply ML models and be able to understand different datatypes and infer it.

Key Takeaways : Theoretical Session (minimal code):

- Introduction to different types of Data,
- Inferential and Descriptive Statistics used in Machine Learning including Measures of Dispersion and Central tendency
- Skewness,Kurtosis, Correlation

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*Session #2:

Topic: Data Pre-processing and EDA

Instructor : Johnson Chetty

Learning Outcomes : Students will be able to clean and standardize data before applying ML models on top of it and understand why it is necessary

Key Takeaways :
- Label encoding and One-hot-encoding categorical variables
- Missing value imputation
- Detecting and removing outliers
- Feature transformation
- Correlation analysis
- Data leakage

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*Session #3

Topic : Logistic Regression

Instructor : Sameer Kumar

Learning Outcomes : Students will learn how to find an extract relevant features in a dataseta and carry out basic binary classification using these features

Key Takeaways :
- Concept of classification in ML
- Using logistic regression for classification problem
- Implementing logistic regression using sklearn

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*Session #4

Topic : Decision Trees

Instructor : Sagar Dawda

Learning Outcome : Understand how decision trees work and what would be the best practices while choosing the appropriate splitting criterion

Key Takeaway : -
- How is it different from Linear models?
- Short intro
- Splitting criteria (explain any one)
- Dataset walkthrough

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A deep insight into Data Science by some of the top Analytics professionals in the industry followed by an hour-long networking with the leaders in Data Science, Analytics and get an opportunity to interact with leaders and your peers in our mixed format sessions. Network with the start-up Founders to see if your skills match what they are looking out for!

The workshop is FREE of cost to attend
RSVP now to reserve your spot at the event!!