AI+ Developer™

Executive Summary

The AI+ Developer™ program offers a complete journey in AI for developers.

• Master Python, advanced concepts, math, statistics, and data analysis.

• Specialize in NLP, Computer Vision, or Reinforcement Learning.

• Learn time series analysis, model explainability, and deployment techniques.

• Earn a certification demonstrating your AI expertise for real-world challenges.

Learning Outcomes

By the end of this program, you will be able to:

• Build and deploy advanced Machine Learning and Deep Learning models.

• Work efficiently with Python libraries like NumPy, Pandas, Matplotlib, Seaborn.

• Apply Computer Vision and NLP techniques in real-world projects.

• Understand and implement Reinforcement Learning in games or robotics tasks.

• Utilize Cloud platforms (AWS, Azure, GCP) for AI development and deployment.

• Work with Large Language Models (LLMs) for text generation, translation, and knowledge extraction.

• Apply Explainable AI, Meta-Learning, and Federated Learning in practical scenarios.

• Present AI projects effectively to technical and non-technical audiences.

• Consider ethics, fairness, transparency, and accountability in AI applications.

Course Modules

Module 1 – Foundations of AI

• Introduction, history, and types of AI

• Functionalities: Reactive Machines, Self-awareness

• Branches: ML, DL, Fuzzy Logic, Generative AI

• Applications: NLP, Computer Vision, Robotics

• Business Use Cases

Module 2 – Mathematics for AI

• Linear Algebra: Vectors, Matrices, Eigenvalues

• Calculus: Derivatives, Gradients, Optimization

• Probability & Statistics: Distributions, Hypothesis Testing

• Discrete Math: Logic, Graph Theory, Combinatorics

Module 3 – Python for AI

• Python fundamentals: Syntax, Control Flow, Data Structures

• Libraries: NumPy, Pandas, Matplotlib, Seaborn

Module 4 – Machine Learning

• Supervised & Unsupervised Learning

• Regression, Classification, Clustering

• Model Evaluation & Selection, Dimensionality Reduction

Module 5 – Deep Learning

• Neural Networks: Architecture & Frameworks

• CNNs: Image Classification

• RNNs: Sequential Data Processing

• Object Detection: YOLO, SSD

Module 6 – Computer Vision

• Image Processing: Filtering, Transformations

• Image Segmentation & GANs

• Hands-on: Medical Images, Autonomous Vehicles

Module 7 – Natural Language Processing (NLP)

• Text Preprocessing: Tokenization, Lemmatization, Word Embeddings

• Text Classification & Sentiment Analysis

• Named Entity Recognition (NER) & Question Answering (QA) Systems

Module 8 – Reinforcement Learning

• Agents, Environments, Rewards

• Q-Learning, Deep Q-Networks, Policy Gradient Methods

• Hands-on: Game AI & Robotics Tasks

Module 9 – Cloud Computing for AI

• Platforms: AWS, Azure, GCP

• Cloud ML Services: AutoML, Pre-trained Models, Deployment

• Hands-on: Cloud-based AI Projects

Module 10 – Large Language Models (LLMs)

• Understanding Architecture, Training, Applications

• Text Generation, Translation, Knowledge Extraction

Module 11 – Advanced AI Research

• Neuro-Symbolic AI

• Explainable AI (XAI)

• Meta-Learning & Few-Shot Learning

• Federated Learning

Module 12 – Communication & Ethics

• Presenting AI Projects

• Documenting AI Systems

• Ethical AI: Bias, Fairness, Transparency, Accountability