Course Description :
This course is designed to deliver valuable content that helps students engage with each other, work on team projects, and become professionals in the AI track. No prior experience is needed, as the course is structured to help beginners from the ground up.
Milestones Overview :
-
Milestone 1 (1-2 months):
- Focus: Python Basics
- Content:
- Syntax, variables, logic operators, if conditions, loops, functions.
- Built-in functions relevant to data science.
- Learning libraries: Numpy, Matplotlib, Seaborn, Pandas.
- Revision of necessary math basics.
- Projects:
- Project 1: Python library project.
- Project 2: Applications for data conversion and visualization.
-
Milestone 2:
- Focus: Feature Engineering and Model Selection in Machine Learning
- Content:
- Overview of machine learning and the data science cycle.
- Handling missing values, data cleaning, and preparation.
- Data visualization techniques.
- Exercises on Kaggle focusing on supervised learning.
- Projects:
- Project 3: Exploratory data analysis library to handle and clean data.
- Project 4: Customer segmentation and competition on Kaggle.
What is Python?
Syntax, Variables, and Logic Operators
If Conditions and Loops
Functions and Built-in Functions
Operators and Sound Effects
Cloning and Randomness
Building a Simple Game
Final Project: Game Development
Overview of Machine Learning
Data Cleaning and Preparation
Handling Missing Values
Data Visualization Techniques
Exercises on Kaggle: Supervised Learning
Exploratory Data Analysis Project
Customer Segmentation
Competition on Kaggle