AI Coding
Learn to code AI solutions, from neural networks to model deployment, while mastering cutting-edge technologies like CNNs, RNNs, and GANs. Perfect for students with basic coding experience, this course provides hands-on projects and real-world applications, equipping you with in-demand AI skills.
Course Program
What you'll learn
- Theoretical and practical aspects of AI including neural networks, deep learning, and model optimization.
- Advanced techniques in computer vision, NLP, GANs, and reinforcement learning.
- Ethical considerations in AI development.
- How to build, deploy, and fine-tune AI models for real-world applications.
Knowledge & skill you'll learn
Course Program
Requirements
- Basic understanding of programming (Python recommended).
- Familiarity with machine learning fundamentals.
- Some experience with mathematical concepts like linear algebra and calculus.
- Beginner Class: For students new to programming or with no prior experience.
- Advance Class: For students with programming basics or who have completed the beginner level.
Course content
Beginner class
Advance class
Beginner class
- Introduction to AI and Machine Learning
Overview of AI, machine learning, and key concepts such as supervised and unsupervised learning. - Setting Up Your AI Development Environment
Installing Python, key libraries like NumPy, Pandas, and TensorFlow or PyTorch. - Python for AI: Basics of Programming
Reviewing essential Python concepts, including loops, functions, and object-oriented programming. - Introduction to Data Science and Preprocessing
Understanding datasets, data cleaning, and preprocessing techniques like normalization and missing value handling. - Introduction to Linear Algebra and Probability
Basic math concepts critical for AI, such as vectors, matrices, and probability theory. - Exploratory Data Analysis (EDA)
Using libraries like Pandas and Matplotlib for visualizing and understanding data patterns. - Supervised Learning: Linear Regression
Implementing linear regression models and understanding their applications. - Supervised Learning: Classification with Logistic Regression
Introduction to logistic regression for binary classification tasks. - Decision Trees and Random Forests
Implementing decision trees and understanding ensemble learning with random forests. - K-Nearest Neighbors and Support Vector Machines
Learning about instance-based algorithms like KNN and margin-based classifiers like SVMs. - Introduction to Neural Networks and Deep Learning
Basics of neural networks, understanding perceptrons, and building a simple neural network. - Working with TensorFlow or PyTorch
Setting up TensorFlow or PyTorch for building neural networks and performing basic tasks. - Unsupervised Learning: Clustering with K-Means
Exploring K-means clustering and applications of unsupervised learning. - Dimensionality Reduction: PCA and t-SNE
Understanding and applying dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE. - Introduction to Natural Language Processing (NLP)
Basics of NLP, tokenization, and working with text data for classification tasks. - Project: Building a Simple AI Model
A project where students build a simple AI model using the concepts learned throughout the course.
Advance class
- Deep Dive into Neural Networks and Backpropagation
Exploring backpropagation, activation functions, and optimizing neural networks. - Advanced Data Preprocessing and Feature Engineering
Advanced techniques in cleaning, transforming, and selecting features for machine learning models. - Convolutional Neural Networks (CNNs)
Introduction to CNNs for image classification, understanding convolution, pooling, and layers. - Recurrent Neural Networks (RNNs) and LSTMs
Working with sequential data using RNNs and LSTMs for time series and text processing. - Transfer Learning and Pre-trained Models
Utilizing pre-trained models like ResNet, BERT, and others for specific tasks. - Generative Adversarial Networks (GANs)
Understanding GANs and building generative models for image synthesis. - Reinforcement Learning: Q-Learning and Deep Q-Networks (DQN)
Introduction to reinforcement learning, exploring Q-learning, and implementing DQNs. - Natural Language Processing (NLP): Advanced Techniques
Working with word embeddings (Word2Vec, GloVe), sequence models, and transformers. - AI for Computer Vision
Advanced applications in image recognition, object detection, and segmentation. - AI in Speech Recognition and Audio Processing
Working with audio data, implementing speech-to-text and voice recognition systems. - Optimizing and Fine-Tuning AI Models
Techniques for hyperparameter tuning, cross-validation, and improving model performance. - AI Model Deployment: From Development to Production
Deploying AI models in production environments using cloud platforms like AWS, Google Cloud, or Azure. - Explainability and Interpretability in AI
Understanding and implementing model interpretability tools like SHAP and LIME for transparent AI. - AI Ethics and Responsible AI Development
Discussing ethical considerations, bias in AI, and how to build responsible AI systems. - AutoML and Neural Architecture Search (NAS)
Introduction to AutoML tools and techniques for automatically selecting and optimizing models. - Capstone Project: Building and Deploying an AI Solution
Students work on a comprehensive project where they build, optimize, and deploy an AI model to solve a real-world problem.
Course Program
Capstone Project
- AI-Based Weather Predictor: Build a simple AI model to predict weather patterns.
- AI Chatbot for FAQs: Create a basic chatbot for answering frequently asked questions.
- Object Recognition Using AI: Design a model to recognize common objects using image data.
- AI-Powered Sentiment Analysis: Develop a tool that analyzes text sentiment from social media posts.
- AI-Based Image Filter: Build a model that applies filters to images based on user preferences
Course Program
Outcomes
- Ability to design and deploy AI models.
- Proficiency in advanced AI tools and frameworks.
- Completion of a capstone AI project.