AI Coding (Beginner, Onsite Course)

Course content

1. Introduction to AI and Machine Learning
   Overview of AI, machine learning, and key concepts such as supervised and unsupervised learning.
2. Setting Up Your AI Development Environment
   Installing Python, key libraries like NumPy, Pandas, and TensorFlow or PyTorch.
3. Python for AI: Basics of Programming
   Reviewing essential Python concepts, including loops, functions, and object-oriented programming.
4. Introduction to Data Science and Preprocessing
   Understanding datasets, data cleaning, and preprocessing techniques like normalization and missing value handling.
5. Introduction to Linear Algebra and Probability
   Basic math concepts critical for AI, such as vectors, matrices, and probability theory.
6. Exploratory Data Analysis (EDA)
   Using libraries like Pandas and Matplotlib for visualizing and understanding data patterns.
7. Supervised Learning: Linear Regression
   Implementing linear regression models and understanding their applications.
8. Supervised Learning: Classification with Logistic Regression
   Introduction to logistic regression for binary classification tasks.
9. Decision Trees and Random Forests
   Implementing decision trees and understanding ensemble learning with random forests.
10. K-Nearest Neighbors and Support Vector Machines
    Learning about instance-based algorithms like KNN and margin-based classifiers like SVMs.
11. Introduction to Neural Networks and Deep Learning
    Basics of neural networks, understanding perceptrons, and building a simple neural network.
12. Working with TensorFlow or PyTorch
    Setting up TensorFlow or PyTorch for building neural networks and performing basic tasks.
13. Unsupervised Learning: Clustering with K-Means
    Exploring K-means clustering and applications of unsupervised learning.
14. Dimensionality Reduction: PCA and t-SNE
    Understanding and applying dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE.
15. Introduction to Natural Language Processing (NLP)
    Basics of NLP, tokenization, and working with text data for classification tasks.
16. Project: Building a Simple AI Model
    A project where students build a simple AI model using the concepts learned throughout the course.

Capstone Project

1. AI-Based Weather Predictor: Build a simple AI model to predict weather patterns.
2. AI Chatbot for FAQs: Create a basic chatbot for answering frequently asked questions.
3. Object Recognition Using AI: Design a model to recognize common objects using image data.
4. AI-Powered Sentiment Analysis: Develop a tool that analyzes text sentiment from social media posts.
5. AI-Based Image Filter: Build a model that applies filters to images based on user preferences

Course Detail : https://iroschool.org/ai-coding/