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Enrichment • Series

AI & Machine Learning Camp

Michael V

Series Details

Sessions

Public Discussion

This series ended on July 5, 2022. All 1:1 and group chats related to this series are disabled 7 days after the last session.

Series Details

About

What? AI is the field of utilizing computers for tasks that require human-like intelligence. Machine learning encompasses algorithms that combine input and output to "teach themselves" how to perform these tasks, without being explicitly programmed to. How? We will begin to learn about AI and its applications, culminating in a walkthrough project with real world applications (visual recognition/deep learning AI). Throughout this course, there will be teacher-led instruction and helpful resources to get you started. Who? This is great for anyone looking to explore AI or start their journey in becoming a machine learning engineer! If you want to be an observer, you are welcome, but you are also welcome to participate in some of the non-mandatory exercises. Prerequisite? None, but I recommend understanding Python for some of the coding examples in the class. Pre-installed Software? None. We will use Google Colaboratory so only a Google Account is needed.

Tutor Qualifications

Over the span of my computer science career, I’ve amassed 22 tech certifications in a wide range of different parts of the field. Several of these certifications are for AI with certified skills in Python, R, Pandas, Machine Learning, Traditional Face Detection, etc. Apart from this, I’ve applied AI in my career, visually analyzing text from consumer data. Nevertheless, I still remember the first days when I started out and how difficult it was to find resources, so I’m happy to welcome any students who are interested in the future of computer science as we know it!

✋ ATTENDANCE POLICY

Each lesson will be consecutive and build on the last one, so try to attend. However, I will post the code and teaching material of each lesson so if you cannot attend, you could always read these resources instead to be ready for the next class. Please give prior notice if you are going to miss any session in order to retain your seat.

Dates

June 21 - July 5

Learners

25 / 30

Total Sessions

5

About the Tutor

I'm excited to be a tutor here!

View Michael V's Profile

Upcoming Sessions

0

Past Sessions

5
21
Jun

Session 1

Orientation

We will start by briefly introducing ourselves and discussing some real world applications of AI that you probably already use everyday (movie recommendations)! We'll also talk about the job roles and future of this field, as well as look at the dataset/problem we'll use throughout the course.
23
Jun

Session 2

Computer Science

We'll begin to explore different machine learning concepts and lingo as well as how this all fits into the field of AI. Students will learn the difference between supervised vs unsupervised learning as well as clustering. The class will also peek into the dizzying medley of different AI models and fields there are like Natural Language processing, Sentiment Analysis, and computer Vision.
28
Jun

Session 3

Computer Science

Getting into building the project AI for this course, students will cover an overview of deep learning (with Tensorflow/Keras) and neural networks. We'll take a look at a simple face detection AI the students can play with and see the fundamental theories behind how computers replicate "vision" through pixels.
30
Jun

Session 4

Computer Science

This session will be the catalyst to understanding the process to code AIs. We'll cover the ML workflow as well as topics like improving performance, false positives, and hyperparameter tuning. The steps students will learn include extracting features, splitting the dataset, training the model, and evaluating the model.
5
Jul

Session 5

Computer Science

Finally, we'll pick up where we left off in the steps of building the AI. The students will get a complete live walkthrough of the process of creating a visual recognition/deep learning AI model, with code. Everything will have a step by step guide from gathering data, to training the model, to evaluating the AI. They can then submit their results and see how they did.

Public Discussion

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