AI & ML Unleashed: From Basics to Deployment!

Name: AI & ML Unleashed: From Basics to Deployment!
Start: 2023-11-22T17:00:00+05:30
End: 2023-11-23T19:30:00+05:30
Location: International Institute of Information Technology Bhubaneswar

International Institute of Information Technology Bhubaneswar, Gothapatna, Bhubaneswar, 751003

International Institute of Information Technology - Bhubaneswar, India

Join this 2-day AI/ML event hosted by GDSC and PSoc where:
Day 1 introduces AI,  ML, Python, key libraries, ML lifecycle, and EDA
Day 2 covers a hands on project, from data import to model deployment. 
Learn the essentials in a concise format and deploy your first ML model!

Nov 22, 2023, 11:30 AM – Nov 23, 2023, 2:00 PM

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Key Themes

Explore MLKaggleML Study JamMachine LearningTensorFlow / Keras

About this event

The upcoming 2-day event on AI/ML hosted by GDSC and PSoc will cover a comprehensive range of topics:
Day 1 will provide an introduction to AI, ML, and Data Science, including discussions on Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. It will also cover the basics of Python programming and essential libraries like NumPy, Pandas, Matplotlib, and Seaborn. The Machine Learning life cycle and the importance of Exploratory Data Analysis (EDA) and Data Preprocessing in the process will be discussed.
Day 2 will have a hands on session on the practical aspects of AI and ML, including importing datasets, performing Exploratory Data Analysis (EDA), data preprocessing, model creation, training, testing, and evaluation. It will also emphasise the critical step of model deployment for real-world applications.
Attendees can expect to gain a holistic understanding of AI and ML concepts and practical insights into the workflow of AI/ML projects.

When

November 22 – 23, 2023
11:30 AM – 2:00 PM UTC

Where

International Institute of Information Technology Bhubaneswar
Gothapatna
Bhubaneswar, 751003

Agenda

Introduction about AI, ML and DS	AI: Creating intelligent machines for human-like tasks, learning from data. ML: Algorithm development for pattern recognition, predictions, and adaptability. DL: Focus on deep neural networks, excelling in image, speech, and NLP tasks. Data Science: Extracting insights from data through statistics, domain knowledge, and ML. Interplay: AI's broader goal, ML and DL techniques, data science foundation, transforming industries.
Basics of Python Programming	Python is a popular high-level programming language known for its simplicity and readability. Python uses indentation to define code blocks, making it easy to read. Variables: How to declare and assign values to variables. Data types: Integers, floats, strings, booleans, and lists. Basic operations: Arithmetic, comparison, and logical operations. Control structures: If statements, loops (for and while). Functions: Defining and using functions in Python.
Exploring Libraries - NumPy, Pandas, Matplotlib, Seaborn	NumPy: A powerful library for numerical operations and working with arrays. Creating NumPy arrays. Basic array operations like element-wise addition and multiplication. Pandas: A library for data manipulation and analysis. Creating and working with DataFrames. Data cleaning, selection, and filtering. Matplotlib: A popular library for data visualization.Creating basic plots, such as line plots, scatter plots, and bar charts. Seaborn: A data visualization library built on top of Matplotlib. Creating more advanced statistical visualizations like heatmaps and pair plots.
ML Life Cycle	1. Problem Definition: Identifying a clear problem that can be solved using machine learning. 2. Data Collection: Gathering relevant data for the problem at hand. 3. Data Preprocessing: Cleaning, handling missing values, and transforming data. 4. Feature Engineering: Selecting and creating meaningful features for the model. 5. Model Selection: Choosing the appropriate machine learning algorithm for the task. 6. Model Training: Using the training data to teach the model to make predictions. 7. Model Evaluation: Assessing the model's performance using various metrics. 8. Hyperparameter Tuning: Optimizing the model's parameters for better results. 9. Model Deployment: Deploying the trained model to make predictions on new data.
Brief About EDA and Data Preprocessing	Exploratory Data Analysis (EDA) involves visually and statistically exploring data to reveal patterns and anomalies. It employs techniques like summary statistics and data visualization, aiding in outlier detection and understanding variable relationships. Data Preprocessing readies raw data for machine learning by cleaning, transforming, and organizing it. This entails handling missing data, addressing outliers, scaling, and converting categorical variables. Feature selection and data splitting for training and testing are also crucial steps. EDA and Data Preprocessing are vital in the machine learning process, enhancing data quality and expediting model development for improved accuracy.

Speakers

Sipra Mohanty

GDSC

Creative and Management Lead

Ayush Anmol

GDSC

ML Associate

Chirantan Beura

GDSC

ML Associate

Tejaswee Kumar Singh

GDSC

ML Associate

Akash Bharti

PSoc

ML Team

Anirudh Pradhan

PSoc

ML Team

Anushka Joshi

PSoc

ML Team

Hitanshu Satapathy

PSoc

ML Team

Priyanshu Tiwari

PSoc

ML Team

Rishit Parija

PSoc

ML Team

Facilitators

Abhipsha Dash

GDSC

Lead

Ravi Kumar

PSoc

Lead

Sai Simran Patro

PSoc

Lead

Organizers

Abhipsha Dash

GDSC Lead

See bio

Anirudh parida

Native App Dev Facilitator

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Pratik Ranjan Sau

Cross Platform App Dev Facilitator

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Sanchita Sahay

Cloud Facilitator

See bio

Swapneel Das

Web Dev Facilitator

See bio

Sipra Mohanty

Creative and Management Lead

See bio

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