AI Bootcamp - Instinct Institute

Introduction

Welcome to AI Bootcamp! This short course design to equip you with essential skills in python for data science and machine learning, you will also diving into the concept data analysis, data preprocessing, data visualization insights and storytelling. Beyond all these essential skills, you will also get hands-on experience with real-world projects, in artificial intelligence and data science.

"The journey of a thousand miles begins with a single step"

Course Criteria

Criteria	Percentage
Attendance	10%
Participation & Quiz	20%
Exam/Capstone Project	40%
Final Project & Presentation	30%

Programming & Tools:

You will use Python throughout this bootcamp with industry-standard tools and libraries:

Development Environment:

Jupyter Notebook

Google Colab

Python

Data Analysis & Visualization:

NumPy

Pandas

Matplotlib

Seaborn

Machine Learning & Deep Learning:

Scikit-learn

TensorFlow

Keras

PyTorch

Course Sessions

Note: The following table will be progressively updated according to course advancement.

Topic	Lab	Solution	Status
1. Python Fundamental	Labs	Solution	`completed!`
2. Python for Data Science	Labs	Solution	`completed!`
3. Introduction to Machine Learning and Capstone Idea	Labs	Solution	`completed!`
3.1. Linear Regression	Labs	Solution	`completed!`
3.2. Logistic Regression	Labs	Solution	`completed!`
4. Supervised Machine Learning
4.1. K-Nearest Neighbors (K-NN)	Labs	Solution	`completed!`
4.2. Decision Tree	Labs	Solution	`completed!`
4.3. Random Forest	Labs	Solution	`completed!`
4.4. Gradient Boosting	Labs	Solution	`completed!`
5. Unsupervised Machine Learning
5.1. Clustering-K-Mean	Labs	Solution	`completed!`
5.2. Principal Component Analysis (PCA)	Labs	Solution	`completed!`
6. Capstone Phase 1	Project	Project	`completed!`
7. Capstone Phase 2	Project	Project	`completed!`
8. API Integration & Git
8.1. Git&Github Fundamental	Labs
8.2. API Integration	Labs
9. Capstone Phase 3: Deployment	Project	Project	`completed!`

Exams and Projects

In this section, you will find all information related to exams, and projects including instructions, starting dates, and deadlines.

Project:

Deadline for the report: ...loading
Where to submit: ...loading
Your report should be in PDF format and include the following:
- Members' names & contributions
  - Clearly state each member's contribution
  - Example: John: Data preprocessing, Jane: Model development
- Introduction: Objectives and purpose of analysis
- Data Preprocessing: Cleaning steps, handling missing values and outliers
- Exploratory Data Analysis (EDA): Visualizations and key patterns
- Model Development: Training, validation, and testing processes
- Results and Evaluation: Performance metrics and insights
- Conclusion: Key findings, limitations, and future work
- References: Cite all sources and libraries used
- Appendix (Optional): Additional figures and code snippets
Presentation: Date ...loading

Resources and Further Reading

Here, you will find additional resources, including books, research papers, and online courses to further your understanding of AI and Data Science.

You will find these resources helpful: