AI+ Developer™

التصنيفات : AiCerts Certifications
قائمتي المفضلة مشاركة
مشاركة
رابط الصفحة
مشاركة على وسائل التواصل الاجتماعي

عن الدورة

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

إظهار المزيد

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