GitHub - NYP-AI/Learning-Materials: A Public Repository containing all of NYP AI’s past event materials
A Public Repository containing all of NYP AI’s past event materials - GitHub - NYP-AI/Learning-Materials: A Public Repository containing all of NYP AI’s past event materials

Ever wanted to get into the AI field, but didn't know where to start?

NYP AI has got you covered! Kickstart: AI is an initiative by NYP AI, a 2-day programme teaching introductory Machine Learning code and concepts to participants.  Our event was held across two days, from 21st - 22nd June 2021.


Day 1: ML Concepts & Libraries

To kick things off, we covered key concepts related to supervised learning, terminologies and real-life applications.

This was followed by an introduction to the core datatypes of Machine Learning - Pandas Dataframes and Numpy Arrays.

Grasping core NumPy concepts

With a clear overview of Supervised Learning and datatypes, we moved on to the modelling stage. In line with our vision to push for Explainable AI, we went down to the essence of Linear Regression - down to the math. This removes the "Black-box" element of Machine Learning algorithms. Understanding how Machine Learning algorithms work helps in understanding their strengths and limitations.

It all boils down to mathematics...

At the end of Day 1, our participants walked away with fresh insights on the Machine Learning landscape, together with a Housing Price Prediction algorithm to showcase their new skillsets.

3 lines...??

Day 2: ML Pipelines

What good does ML do if we can't apply it properly? That's what we served to explain on Day 2. Covering popular approaches to Machine Learning projects, we set forth equipping our participants with the right ML techniques and strategies.

We covered the core Data Pipelines, namely: Data Ingestion, Data Exploration, Data Cleaning, and Feature Enginering.

Data Exploration with Pandas Dataframes
One Hot Encoding for categorical values

This was followed by core Modelling Pipelines: Data Splitting, Model Training, Model Evaluation and Hyperparameter Tuning.

Splitting into Training and Validation sets
Logistic Regression classification algorithm
NvrOverfit and NvrUnderfit......

With a coherent and comprehensive code walkthrough, our participants were able to consolidate their knowledge.

Our notebook for Day 2

Till the next Time ~

The team at NYP AI is glad that many participants have gained much knowledge on Machine Learning. We'd like to thank all participants for the overwhelmingly positive feedback and insightful suggestions 🤗. Do keep a lookout on our socials https://www.instagram.com/nyp_ai/ for updates on our latest events :)