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Introduction to Machine Learning and Deep LearningExpired

12 sessions starting on Mar 29

What You'll Do:

This course will build on concepts learnt in the Intro to Python and Data Science course.

You will start with an overview of Machine Learning concepts using scikit-learn and learn techniques such as Linear Regression, Classification using nearest neighbors, random forests and bayesian models and Clustering using k-means clustering. You will also learn some preprocessing, dimensionality reduction and testing and validation techniques before diving into deep learning. 

PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models.

By the end of the course, you will have built state-of-the art Deep Learning and Computer Vision applications with PyTorch.  

You will learn how to work with Tensors, build neural networks from scratch, build complex neural models for image recognition, use pretrained models for solving Computer Vision problems and use style transfer to build sophisticated AI models. This course will introduce you to the fundamental ideas behind Convolutional Neural networks(CNN) and Recurrent neural networks(RNN), This course will take an application oriented approach that will help you build models to solve real world problems and you will demonstrate this learning by a capstone project. 

Skill Level:

Intro to Python and Data Science (or equivalent experience). Familiarity with Colab (or Jupyter) and github. Familiarity with Math at Algebra/Pre-calculus level. A computer with internet access and a compatible browser needed for accessing and running the code.

Skills you will learn:

Pytorch, scikit-learn, Machine Learning, Deep Learning, CNN, RNN, Classification, Clustering, Regression

About your Facilitator:

Show and Tell (Presentation or Demonstration)

  • Sheroes Tech

    Sheroes is a non-profit organization founded by Bay Area high schooler Kavya Narayan. At Sheroes Tech, qualified instructors teach girls ages 10-18 computer programming. Our classes work in 10 week sessions, each session being an introduction to even deeper programming topics.

    Kavya is currently a Junior at Saratoga High School in the Bay Area. She started Sheroes back in 2017 because of her passion to inspire other girls like herself to get into tech. In addition to working as the founder of Sheroes, she also tutors students in preparation for their own AP Computer Science Classes.

    Vidya Rangasayee is an experienced software engineer and is deeply passionate about Women in Tech, K-12 and higher education and encouraging girls in STEM. She has years of experience at Google, Amazon and Paypal and also at Bay Area startups and she brings this know-how into the classroom to make it not only educational but also practical and fun. She spent several years as a lecturer of Computer Science at San Jose State University and is now taking her passion for teaching one step further by pursuing a PhD in Music and AI at Stanford University, where she completed her Masters in Computational Mathematics. Her plans are to take up a full time teaching position upon completing her doctorate.


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Ticket - Introduction to Machine Learning and Deep Learning
  • Mar 29, 2021 9:00 am - 11:00 am (Pacific)
  • Mar 30, 2021 9:00 am - 11:00 am (Pacific)
  • Mar 31, 2021 9:00 am - 11:00 am (Pacific)
  • Apr 1, 2021 9:00 am - 11:00 am (Pacific)
  • Apr 2, 2021 9:00 am - 11:00 am (Pacific)
  • Apr 3, 2021 9:00 am - 11:00 am (Pacific)
  • Apr 4, 2021 9:00 am - 11:00 am (Pacific)
  • Apr 5, 2021 9:00 am - 11:00 am (Pacific)
  • Apr 6, 2021 9:00 am - 11:00 am (Pacific)
  • Apr 7, 2021 9:00 am - 11:00 am (Pacific)
  • Apr 8, 2021 9:00 am - 11:00 am (Pacific)
  • Apr 9, 2021 9:00 am - 11:00 am (Pacific)
Age Range: 18+ 14-18 12-14
Have questions or comments? Email us at makercampus@make.co

Maker Campus session recordings will be made available to all paid registrants.
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