Artificial Intelligence / Machine Learning 3 Days Workshop in Darwin

Certificate: Course Completion Certificate

Language: English

Duration: 3 Days

Credits: 24

Refreshments: Snacks, Beverages and Lunch included in a classroom session

Course Delivery: Classroom

Course Description:

This course provides a comprehensive introduction to the exciting fields of Artificial Intelligence (AI) and Machine Learning (ML). Designed for students with varying levels of expertise, from beginners to those with some background in programming and mathematics, the course covers fundamental concepts, techniques, and applications of AI and ML.

Attendees will begin by exploring the foundational principles of AI, including its history, goals, and various approaches. They will gain an understanding of intelligent agents, problem-solving techniques, and the importance of knowledge representation. Emphasis will be placed on the ethical considerations surrounding AI development and deployment.

The course will then delve into the core concepts of Machine Learning, where students will learn about different types of learning algorithms, such as supervised, unsupervised, and reinforcement learning. Through hands-on exercises and projects, students will develop a practical understanding of how to apply these algorithms to real-world problems, including classification, regression, clustering, and reinforcement learning tasks.

Learning Objectives:

After the training, you will be able to:

• Describe Artificial Intelligence workloads and considerations
• Describe fundamental principles of machine learning
• Describe features of computer vision workloads
• Describe features of Natural Language Processing (NLP) workloads
• Describe features of conversational AI workload

Course Outline:

Session I: Introduction to Artificial Intelligence

• 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

• Importance of AI
• AI vs Human Intelligence
• Building Blocks of AI
• AI Trends
• AI Statistics
• Key Takeaways
• Let’s Test What We Have Learnt

Session II: Application of Artificial Intelligence

• Applications of Artificial Intelligence in

o Marketing

o Finance

o Defense & Military

o Telecommunication

o Sales

o Healthcare

o Automobile Industry

o Gaming

o E-Commerce Industry

o Social Media

o Robots

o Education Sector

o Chatbots

o Agriculture

o Supply Chain

o Navigation

o Lifestyle

o Human Resources

• Key Takeaways
• Let’s Discuss

Session III: Core Concepts of Machine Learning

• Overview of Machine Learning
• History of Machine Learning
• Machine Learning Algorithms

o Supervised Learning

§ Regression Models in ML

§ Introduction to Regression Models

§ Types of Regression Models

§ Linear Regression

§ Polynomial Regression

§ 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

o Unsupervised Learning

§ 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

§ Applications of Association Rule Learning

§ Hidden Markov Model

o Reinforcement Learning

Importance of Machine Learning

• Steps in Machine Learning

o Data Collection

o Data Preparation

o Choosing a Model

o Training the Model

o Evaluating the Model

o Parameter Tuning

o Making Predictions

• Advantages of Machine Learning
• Disadvantages of Machine Learning
• Future of Machine Learning
• Key Takeaways
• Let’s Test What We Have Learnt

Session IV: Use Cases of Machine Learning

• Introduction to Automatic Language Translation using ML

o Google Translate

o Microsoft Translate

o Facebook Translator

o Limitations of Automatic Language Translator

• Introduction to Medical Diagnosis Using ML

o Objectives of ML-powered Medical Diagnosis

o Benefits of ML-powered Medical Diagnosis

o Applications of ML-powered Medical Diagnosis

o Organizations using ML for Medical Diagnosis

• Introduction to Image Recognition using ML

o Working of Image Recognition

o ML Image Recognition Models

o Image Recognition Application for Face Analysis

o Image recognition Application for Animal Monitoring

• Introduction to Speech Recognition using ML

o Speech Recognition System

o Key Features of Speech Recognition

o Speech Recognition Algorithms

o Speech Recognition with Machine Learning Use Case: IBM

• Key Takeaways
• Let’s Test What We Have Learnt

Session V: Deep Learning in a Nutshell

• Introduction to Deep Learning
• Importance of Deep Learning
• Working of Deep Learning
• Machine Learning vs Deep Learning
• Functions of Deep Learning

o Sigmoid Activation Function

o Hyperbolic Tangent Function

o ReLU

o Loss Functions

§ Mean Absolute Error

§ Mean Squared Error

§ Hinge Loss

§ Cross-Entropy

o Optimizer Functions

§ Stochastic Gradient Descent

§ Adagrad

§ Adadelta

§ Adaptive Moment Estimation

• Deep Learning Process

o Working of Deep Learning

o Deep Neural Network

o Deep Learning Technique

o How to Create Deep Learning models?

o Two Phases of Learning

• Advantages of Deep Learning
• Applications of Deep Learning

o Detecting Developmental Delay in Children

o Colorization of Black and White Images

o Adding sound to Silent Movies

o Pixel Restoration

o Sequence Generation

o Toxicity testing for chemical structures

o Radiology/Detection of mitosis

o Market Prediction

o Fraud Detection

o Earthquake Prediction

o Deep Fakes

• Limitations of Deep Learning
• Key Takeaways
• Let’s Discuss

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

• Introduction to Natural Language Processing (NLP)

o Understanding NLP

o NLP Techniques

o Working of NLP

o Importance of NLP

o Steps of NLP

§ Lexical Analysis

§ Syntactic Analysis

§ Semantic Analysis

§ Discourse Integration

§ Pragmatic Analysis

o Applications of NLP

• Introduction to Natural Language Generation (NLG)

o Working of NLG

o Applications of NLG

o Advantages of NLG

• Introduction to Natural Language Understanding (NLU)

o NLP vs NLU

o NLU Use Cases

§ Automatic Ticket Routing

§ Automated Reasoning

§ Machine Translation

§ Question Answering

o Importance of NLU

o Factors to Consider while selecting NLU solutions

o Evaluating the accuracy of NLU solutions

o Leading NLU Companies

• NLP vs NLG vs NLU
• AI vs ML
• ML vs DL
• AI vs ML vs DL
• Key Takeaways

Session VII: Hybrid Artificial Intelligence – Machine as Creative Partners

• Overview of Hybrid Model
• Pros and Cons of Hybrid Model
• Introduction to ANN (Artificial Neural Network)

o Layers in a Neural Network

o Neurons in a Neural Network

o Activation Function in a Neural Network

o Threshold Function in a Neural Network

o Sigmoid Function in a Neural Network

o Rectifier Function in a Neural Network

o Hyperbolic Tangent Function in a Neural Network

o Working of a Neural Network

o Introduction to Gradient Descent

§ Types of Gradient Descent

o Introduction to Backpropagation

§ Advantages of Backpropagation

§ Disadvantages of Backpropagation

o Advantages of ANN

o Disadvantages of ANN

• Introduction to CNN (Convolutional Neural Network)

o Working of CNN

§ Convolutional layer in CNN

§ Hyperparameters of Convolutional Layer in CNN

§ Pooling Layer in CNN

§ Fully-Connected Layer in CNN

• Introduction to Autoencoders

o Components of Autoencoders

o Types of Autoencoders

o Applications of Autoencoders

• Introduction to Variational Autoencoders
• Introduction to Feedforward Neural Networks

Introduction to Recurrent Neural Networks (RNN)

o Recurrent Neural Network vs Feedforward Neural Network

o Why RNN?

o Problems with RNN

o Types of RNN

o Variants of RNN Architectures

§ Bidirectional RNN

§ Long Short-Term Memory

§ Gated Recurrent Units

o Advantages of RNN

o Disadvantages of RNN

o Applications of RNN

• Introduction to Mixture Density Network

o Components of Mixture Density Networks

o How does a Mixture Density Network look like?

• Key Takeaways
• Let’s Discuss

Session VIII: Essentials of Successful AI Strategy for Business

• AI Strategies for Business Outcomes
• Evaluating Current Capacities of AI
• Building an AI Strategy
• Roadmap For Building a Viable AI Strategy
• Strategy for AI Business Models
• Five-Step Implementation Plan for AI
• AI Assessment Roadmap
• Top AI Job Roles
• Myths and Facts around AI
• ABCDE Framework for AI Enterprise Strategy
• Key Takeaways
• Let’s Discuss

Prerequisites:

Prerequisite is not required for taking this course. Anyone can with some basic awareness of computing and internet concepts, and an interest in using AI services.

Specifically:

• Experience using computers and the internet.
• Interest in use cases for AI applications and machine learning models.
• A willingness to learn through hands-on exploration.

Who can Attend?

The Artificial Intelligence / Machine Learning course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible. You don’t need to have any experience of using AI before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts.

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Course Contents:

Expert Training to Improve Performance Management

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