June 13 (F)

TOPIC	DESCRIPTION
Introduction (15 mins)	> Meet the instructor.
Python Fundamentals (180 mins)	> Set up cloud notebook environment. > Explore Python syntax, variables, and data types. > Implement functions with arguments and parameters. > Control flow using conditionals and loops. > Utilize built-in data structures and third-party packages.
Break (60 mins)
Exploratory Data Analysis (60 mins)	> Introduction to Numpy and Pandas. > Dataset and the importance of Data types. > Overview of Statistical concepts, reading and analyzing of data.
Break (15 mins)
Data Handling, Processing, Visualization, and Modelling (150 mins)	> Learn fundamental data handling concepts. > Wrangle data using split-apply-transform. > Preprocess data for modeling and visualization. > Build and train simple predictive models. > Evaluate model performance metrics.
Break (15 mins)
Capstone Project (90 mins)	> Complete the hands-on projects for data modeling: > Predicting Fuel Consumption of Automobiles

June 14 (Sa)

TOPIC	DESCRIPTION
Introduction (15 mins)	> Quick recap of the previous day
Mechanics of Deep Learning (120 mins)	> Train a computer vision model to learn the process of training. > Introduction of convolutional neural networks to improve accuracy. > Applying data augmentation to enhance datasets and model generalization.
Break (15 mins)
Pre-trained Models (120 mins)	> Integrating pre-trained image classification model > Training the model to autocomplete a text based on a prompt.
Break (60 mins)
Object Classification (120 mins)	> Applying computer vision to distinguish two different objects > Discuss advanced neural network architectures and recent areas of research where students can further improve their skills.
Break (15 mins)
Final Project (120 mins)	> Review key learnings and answer questions. > Complete the assessment and earn a certificate.