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Get hands-on with the tools and technologies that power the AI ecosystem. Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design Hands-on Proj...
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Successfully delivered 49 sessions for over 91 professionals

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Skills You’ll Gain

  • Python for AI Development
  • Advanced Mathematics and Statistics
  • Optimization Techniques
  • Deep Learning Fundamentals
  • Data Processing and Exploratory Analysis
  • NLP, Computer Vision, or Reinforcement Learning Specialization
  • Time Series Analysis
  • Model Explainability and Deployment
  • Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.
  • Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.
  • Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.
  • IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.
  • Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.
  • Basic math, including familiarity with high school-level algebra and basic statistics, is desirable.
  • Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential.
  • A fundamental knowledge of programming skills is required.

50 questions, 70% passing, 90 minutes, online proctored exam

  1. Course Introduction

  1. 1.1 Introduction to AI
  2. 1.2 Types of Artificial Intelligence
  3. 1.3 Branches of Artificial Intelligence
  4. 1.4 Applications and Business Use Cases

  1. 2.1 Linear Algebra
  2. 2.2 Calculus
  3. 2.3 Probability and Statistics
  4. 2.4 Discrete Mathematics

  1. 3.1 Python Fundamentals
  2. 3.2 Python Libraries

  1. 4.1 Introduction to Machine Learning
  2. 4.2 Supervised Machine Learning Algorithms
  3. 4.3 Unsupervised Machine Learning Algorithms
  4. 4.4 Model Evaluation and Selection

  1. 5.1 Neural Networks
  2. 5.2 Improving Model Performance
  3. 5.3 Hands-on: Evaluating and Optimizing AI Models

  1. 6.1 Image Processing Basics
  2. 6.2 Object Detection
  3. 6.3 Image Segmentation
  4. 6.4 Generative Adversarial Networks (GANs)

  1. 7.1 Text Preprocessing and Representation
  2. 7.2 Text Classification
  3. 7.3 Named Entity Recognition (NER)
  4. 7.4 Question Answering (QA)

  1. 8.1 Introduction to Reinforcement Learning
  2. 8.2 Q-Learning and Deep Q-Networks (DQNs)
  3. 8.3 Policy Gradient Methods

  1. 9.1 Cloud Computing for AI
  2. 9.2 Cloud-Based Machine Learning Services

  1. 10.1 Understanding LLMs
  2. 10.2 Text Generation and Translation
  3. 10.3 Question Answering and Knowledge Extraction

  1. 11.1 Neuro-Symbolic AI
  2. 11.2 Explainable AI (XAI)
  3. 11.3 Federated Learning
  4. 11.4 Meta-Learning and Few-Shot Learning

  1. 12.1 Communicating AI Projects
  2. 12.2 Documenting AI Systems
  3. 12.3 Ethical Considerations

  1. 1. Understanding AI Agents
  2. 2. Case Studies
  3. 3. Hands-On Practice with AI Agents

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Email: felice.deangelis@proficegroup.it

Whatsapp: +39 393 390 9401

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