Course Description:

This workshop offers a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML), suitable for participants ranging from beginners to those with some programming and mathematical background. It covers fundamental concepts, techniques, and practical applications of AI and ML.

Participants will explore the foundational principles of AI, including its history, various approaches, and ethical considerations. They will delve into Machine Learning concepts such as supervised, unsupervised, and reinforcement learning, and gain hands-on experience applying these algorithms to real-world problems.

Learning Objectives:

After completing this workshop, participants will be able to:

• Describe AI workloads and considerations
• Explain fundamental principles of machine learning
• Discuss computer vision and Natural Language Processing (NLP) workloads
• Explore conversational AI workloads

Course Outline:

Session I: Introduction to Artificial Intelligence

• Introduction to AI
• History and types of AI
• AI vs Human Intelligence
• Ethical considerations in AI
• AI trends and statistics

Session II: Application of Artificial Intelligence

• AI applications in various sectors
• Discussion on AI’s impact and future implications

Session III: Core Concepts of Machine Learning

• Overview and history of ML
• Supervised, unsupervised, and reinforcement learning algorithms
• Practical applications of ML algorithms

Session IV: Use Cases of Machine Learning

• Applications in language translation, medical diagnosis, image and speech recognition

Session V: Deep Learning in a Nutshell

• Introduction and importance of deep learning
• Deep learning techniques and applications

Session VI: Fundamentals of Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU)

• NLP techniques and applications
• Introduction to NLG and NLU

Session VII: Hybrid Artificial Intelligence – Machine as Creative Partners

• Overview of hybrid models
• Introduction to neural networks, CNNs, and RNNs
• Applications and limitations of deep learning

Session VIII: Essentials of Successful AI Strategy for Business

• Developing AI strategies for business outcomes
• Implementing AI strategies and assessing AI capabilities

Prerequisites: No prerequisites required. Basic familiarity with computing and the internet is recommended.

Who Can Attend? This workshop is ideal for anyone interested in exploring the potential of AI and machine learning, regardless of prior experience. Basic understanding of computing concepts and an interest in AI applications are beneficial.

Day 1:

9:00 AM – 9:30 AM: Introduction to the Workshop

Session I: Introduction to Artificial Intelligence

• 9:30 AM – 10:30 AM:
  • Introduction to Artificial Intelligence
  • History of AI
  • Types of AI
    • Based on Functionality
    • Based on Capabilities (Artificial Narrow Intelligence, Artificial General Intelligence, Artificial Super Intelligence)
• 10:30 AM – 10:45 AM: Morning Break
• 10:45 AM – 12:45 PM:
  • Importance of AI
  • AI vs Human Intelligence
  • Building Blocks of AI
  • AI Trends
  • AI Statistics
  • Key Takeaways
  • Let’s Test What We Have Learnt
• 12:45 PM – 1:00 PM: Q&A and Discussion
• 1:00 PM – 1:30 PM: Lunch Break

Session II: Application of Artificial Intelligence

• 1:30 PM – 3:30 PM:
  • Applications of AI in:
    • Marketing
    • Finance
    • Defense & Military
    • Telecommunication
    • Sales
    • Healthcare
    • Automobile Industry
    • Gaming
    • E-Commerce Industry
• 3:30 PM – 3:45 PM: Evening Break
• 3:45 PM – 4:45 PM:
  • Applications of AI in:
    • Social Media
    • Robots
    • Education Sector
    • Chatbots
    • Agriculture
    • Supply Chain
    • Navigation
    • Lifestyle
    • Human Resources
• 4:45 PM – 5:00 PM: Key Takeaways and Discussion 

Day 2:

Session III: Core Concepts of Machine Learning

• 9:00 AM – 10:00 AM:
  • Overview of Machine Learning
  • History of Machine Learning
  • Supervised Learning Algorithms
    • Regression Models in ML
    • Introduction to Regression Models
    • Types of Regression Models
    • Linear Regression
    • Polynomial Regression
• 10:00 AM – 10:15 AM: Morning Break
• 10:15 AM – 12:45 PM:
  • Regression Models (continued)
    • Ridge Regression
    • Lasso Regression
    • Bayesian Regression
    • Overview of Decision Trees
    • Overview of Random Forest Algorithm
  • Classification Models in ML
    • Logistic Regression
    • KNN Algorithm
    • Naive Bayes Algorithm
    • SVM Algorithm
• 12:45 PM – 1:00 PM: Q&A and Discussion
• 1:00 PM – 1:30 PM: Lunch Break
• 1:30 PM – 3:30 PM:
  
  • Unsupervised Learning Algorithms
    • Clustering in ML
    • Types of Clustering in ML
      • Partitioning Clustering
      • Density-Based Clustering
      • Distribution Model-Based Clustering
      • Hierarchical Clustering
      • Fuzzy Clustering
    • Overview of Clustering Algorithms in ML
    • Types of Clustering Algorithms
      • K-means Algorithm
      • Mean-Shift Algorithm
      • DBSCAN Algorithm
      • Expectation-Maximization Clustering using GMM
      • Agglomerative Hierarchical Algorithm
      • Affinity Propagation
    • Application of Clustering
    • Association Rule Learning
      • Apriori Algorithm
      • Eclat Algorithm
      • F-P Growth Algorithm
• 3:30 PM – 3:45 PM: Evening Break
• 3:45 PM – 4:45 PM:
  • Hidden Markov Model
  • Reinforcement Learning
  • Importance of Machine Learning
  • Steps in Machine Learning
    • Data Collection
    • Data Preparation
    • Choosing a Model
    • Training the Model
    • Evaluating the Model
    • Parameter Tuning
    • Making Predictions
  • Advantages and Disadvantages of ML
  • Future of Machine Learning
  • Key Takeaways
  • Let’s Test What We Have Learnt
• 4:45 PM – 5:00 PM: Key Takeaways and Discussion

Day 3:

Session IV: Use Cases of Machine Learning

• 9:00 AM – 10:00 AM:
  • Introduction to Automatic Language Translation using ML
    • Google Translate
    • Microsoft Translate
    • Facebook Translator
    • Limitations of Automatic Language Translator
  • Introduction to Medical Diagnosis Using ML
    • Objectives and Benefits of ML-powered Medical Diagnosis
    • Applications and Organizations using ML for Medical Diagnosis
• 10:00 AM – 10:15 AM: Morning Break
• 10:15 AM – 12:45 PM:
  • Introduction to Image Recognition using ML
    • Working of Image Recognition
    • ML Image Recognition Models
    • Applications for Face Analysis and Animal Monitoring
  • Introduction to Speech Recognition using ML
    • Speech Recognition System
    • Key Features and Algorithms
    • Use Case: IBM
• 12:45 PM – 1:00 PM: Q&A and Discussion
• 1:00 PM – 1:30 PM: Lunch Break

Session V: Deep Learning in a Nutshell

• 1:30 PM – 2:30 PM:
  • Introduction to Deep Learning
  • Importance of Deep Learning
  • Working of Deep Learning
  • Machine Learning vs Deep Learning
  • Functions of Deep Learning
    • Sigmoid Activation Function
    • Hyperbolic Tangent Function
    • ReLU
    • Loss Functions (Mean Absolute Error, Mean Squared Error, Hinge Loss, Cross-Entropy)
    • Optimizer Functions (Stochastic Gradient Descent, Adagrad, Adadelta, Adaptive Moment Estimation)
• 2:30 PM – 3:30 PM:
  • Deep Learning Process
    • Working of Deep Learning
    • Deep Neural Network
    • Deep Learning Technique
    • Creating Deep Learning Models
    • Two Phases of Learning
  • Advantages and Disadvantages of Deep Learning
  • Applications of Deep Learning
    • Detecting Developmental Delay in Children
    • Colorization of Black and White Images
    • Adding sound to Silent Movies
    • Pixel Restoration
    • Sequence Generation
    • Toxicity Testing for Chemical Structures
    • Radiology/Detection of Mitosis
    • Market Prediction
    • Fraud Detection
    • Earthquake Prediction
    • Deep Fakes
• 3:30 PM – 3:45 PM: Evening Break
• 3:45 PM – 4:45 PM: Session VI: Fundamentals of NLP, NLG, and NLU
  • Introduction to Natural Language Processing (NLP)
    • Understanding NLP
    • NLP Techniques
    • Working and Importance of NLP
    • Steps of NLP (Lexical Analysis, Syntactic Analysis, Semantic Analysis, Discourse Integration, Pragmatic Analysis)
    • Applications of NLP
  • Introduction to Natural Language Generation (NLG)
    • Working of NLG
    • Applications and Advantages of NLG
  • Introduction to Natural Language Understanding (NLU)
    • NLP vs NLU
    • NLU Use Cases (Automatic Ticket Routing, Automated Reasoning, Machine Translation, Question Answering)
    • Importance of NLU
    • Selecting and Evaluating NLU Solutions
    • Leading NLU Companies
  • NLP vs NLG vs NLU
  • AI vs ML
  • ML vs DL
  • AI vs ML vs DL
  • Key Takeaways

4:45 PM – 5:00 PM: Closing Discussion and Final Q&A 

Frequently Asked Questions (FAQs) 

Q1. What is the Artificial Intelligence / Machine Learning Workshop?

• It’s a 3-day training program introducing participants to AI and ML fundamentals and applications.

Q2. Who should attend this workshop?

• Anyone interested in AI and ML, with or without prior experience, but with a basic understanding of computer technology and mathematics.

Q3. Will I receive a certificate upon completion?

• Yes, participants will receive a certificate of completion.

Q4. What is the duration and format of the workshop?

• The workshop runs for 3 days, from 9 AM to 5 PM, and is available in classroom, virtual, and on-site formats.

Q5. How many PDUs can I earn from this workshop?

• Participants will earn 24 Professional Development Units (PDUs).

Q6. Are there any prerequisites for attending this workshop?

• No formal prerequisites, but basic computing skills and interest in AI are beneficial.

Q7. What kind of hands-on activities are included?

• Practical exercises and projects applying machine learning algorithms to real-world problems.

Q8. How can AI and ML skills benefit my career?

• AI and ML skills are in high demand and can enhance career prospects in various industries.

Q9. What resources and materials will be provided?

• Comprehensive course materials, including lecture notes and practical exercise guides, will be provided.

Q10. How do I enroll in the workshop?

• Enroll by visiting our website and registering online. For queries, contact our support team via email or phone. 

Benefits of Choosing This Course for Your Team:

• Advanced Technical Proficiency:

• Your team will master AI and ML, equipping them with advanced technical skills that are highly sought after in the industry.

• Data-Driven Culture:

• Foster a data-driven culture where decisions are based on insights and analytics, leading to more strategic and effective outcomes.

• Innovative Project Development:

• Empower your team to create innovative projects and solutions, leveraging AI and ML to solve complex challenges creatively.

• Competitive Advantage:

• Stay ahead of competitors by adopting cutting-edge AI and ML technologies, positioning your organization as a leader in your field.

• Customized Learning Paths:

• Tailor learning experiences to the specific needs and interests of your team, ensuring relevant and impactful skill development.

• Comprehensive Ethical Framework:

• Equip your team with a thorough understanding of ethical considerations in AI, ensuring responsible and fair implementation of technologies.

• Collaboration and Networking:

• Enhance your team’s professional network by connecting with AI and ML experts and peers, fostering collaboration and knowledge exchange.

• Resource Optimization:

• Utilize AI to optimize resource allocation and management, resulting in significant cost savings and operational efficiency.

• Enhanced Analytical Capabilities:

• Improve your team’s ability to analyze and interpret data, leading to more accurate and insightful business decisions.

• Long-Term ROI:

• Investing in AI and ML training yields long-term returns through continuous innovation, improved processes, and sustained competitive advantage.

• Market Leadership:

• Solidify your position as a market leader by continuously adopting and integrating the latest AI and ML advancements.

• Customer Delight:

• Improve customer satisfaction and loyalty through personalized and efficient AI-powered services and solutions.

• Scalable Solutions:

• Implement scalable AI and ML solutions that can grow with your organization, ensuring flexibility and adaptability in an evolving market.

• Risk Mitigation:

• Utilize AI to identify and mitigate risks proactively, safeguarding your organization against potential threats and uncertainties.

• Sustainable Growth:

• Foster sustainable growth by leveraging AI and ML to optimize operations, innovate processes, and drive long-term success.

• Future-Ready Workforce:

• Prepare your workforce for future technological advancements, ensuring your organization remains agile and resilient in a rapidly changing landscape.

Dates: 

State	City	Date	Early Bird Date	Standard Fee	Early Bird Fee	Venue	Enroll Now
Texas	Austin	Nov 19th - 21st, 2024	Nov 09th, 2024	USD 1995.00	USD 1795.00	Venue	Processing ... Register Now
Texas	Austin	Dec 17th - 19th, 2024	Dec 07th, 2024	USD 1995.00	USD 1795.00	Venue	Processing ... Register Now