Price: $3,999.00
Length: 3 Days
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Artificial Intelligence Training Bootcamp, Artificial Intelligence  A-Z

Artificial Intelligence Training Bootcamp covers the fundamentals of Artificial Intelligence (AI), Machine Learning, Deep Learning, Neural Networks, Sensor Fusion, and other AI concepts. Participants will work with Artificial Intelligence Tools, AI Programming Tools, Data Science Tools, Advanced Analytics Tools, and Machine and Deep Learning algorithms and methods.

AI programming languages and tools to design intelligent agents, deep learning algorithms, neural networks, advanced AI networks are explored to resolve real-time decision-making issues.

Artificial intelligence (AI)  discipline covers anything related to making machines smart related to robotics, autonomous driving, IoT or software application. if you are making them smart, then it’s AI. Machine Learning (ML) is a subset of AI dealing with systems that can learn by themselves (we cover both supervised and unsupervised learning principles in this course). Using AI and Machine Learning, Systems, System of Systems (SoS) and more complex capabilities get smarter and smarter over time without human intervention. Deep Learning (DL) is basically the same as ML but applied to large data sets. Most AI work now involves ML because intelligent behavior requires considerable knowledge, and learning is the easiest way to get that knowledge.

Who Should Attend?
Engineers, project managers, analysts and anyone else interested in Artificial Intelligence A-Z.

Learning Objectives

After completing this course, the participants will:

  • Gain a basic understanding of key Artificial Intelligence (AI) concepts
  • List key principles behind Artificial Intelligence (AI)
  • List key applications and use cases of Artificial Intelligence
  • Compare and contrast Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Neural Networks
  • List applications of between AI, Machine Learning and Data Mining
  • List AI and Machine Learning concepts, principles, algorithms, tools and applications
  • Analyze classical Artificial Intelligence techniques such as fraud detection, neural networks and control systems
  • Learn how Artificial Intelligence uses data to offer solutions to existing problems
  • Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn, adapt and optimize
  • Clarify how Data Mining can serve as foundation for AI and machine learning to use existing information to highlight patterns
  • List the various applications of artificial intelligence and related algorithms
  • Learn how to classify the types of learning such as supervised and unsupervised learning
  • Implement supervised learning techniques such as linear and logistic regression
  • Use unsupervised learning algorithms including deep learning, clustering and recommender systems (RS)
  • Learn about classification data and and learning models
  • Select the best algorithms and tools applied to artificial intelligence
  • Make accurate predictions and analysis to effectively solve potential problems
  • Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering
  • Comprehend the theoretical concepts and how they relate to the practical aspects of artificial intelligence
  • Analyze robust machine learning and deep learning algorithms including clustering and recommendation systems

Course Agenda and Topics

Core Concepts and Techniques behind Artificial Intelligence (AI)

  • Fundamentals of Artificial Intelligence (AI)
  • Introduction to Artificial Intelligence (AI)
  • Applications of AI
  • Fraud Detection
  • Image Processing
  • Computer Vision
  • Robotics and Robot Motion Planning
  • Network Security
  • Cybersecurity Attack Detection
  • Machine Learning: Supervised and Unsupervised Learning

Data Science Overview

  • Data Science with Python
  • Data Analytics
  • Data
  • Analysis
  • Prediction
  • Recommendation
  • Building Smart Chatbots Powered by AI

Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP)

  • Key Artificial Intelligence (AI) Principles Applied
  • Broad discipline of creating intelligent machines, systems, system of systems (SoS) and intelligent capabilities
  • AI-powered machines
  • General and narrow intelligence AI machines
  • Performing human tasks intelligently
  • Machine Learning (ML)
  • Systems or SoS that can learn from experience both supervised and unsupervised
  • Deep Learning (DL)
  • Systems that learn from experience on large data sets
  • Artificial Neural Networks (ANN)
  • Models of human neural networks to assist machines and computers to learn
  • Natural Language Processing (NLP)
  • Smart systems that can understand language

Review of AI Terminology and Principles

  • Math Refresher
  • Concepts of linear algebra
  • Probability and statistics
  • Algorithms
  • Automation and iterative processes
  • Scalability
  • Ensemble modeling
  • Framing
  • Generalization
  • Machine Learning methods
  • Classification
  • Training and Training Set
  • Validation
  • Representation
  • Regularization
  • Logistic Regressions
  • Neutral Nets
  • Neutral Nets
  • Multi class Neutral Nets
  • Embeddings
  • Basic Algebra and Calculus
  • Basic Python
  • Chain rule
  • Concept of a derivative
  • Gradient or slope
  • Linear algebra
  • Logarithms, and logarithmic equations
  • Matrix multiplication
  • Mean, median, outliers and standard deviation
  • Partial derivatives
  • Sigmoid function
  • Statistics
  • Tanh
  • Tensor and tensor rank
  • Trigonometry
  • Variables, coefficients, and functions

Applied Artificial Intelligence (AI) and Machine Learning

  • Machine Learning prediction with models
  • Artificial Intelligence behaving and reasoning
  • Applications of Machine Learning
  • Machine Learning algorithms
  • Models
  • Techniques
  • Statistics and Math
  • Algorithms
  • Programming
  • Patterns and Prediction
  • Intelligent Behavior
  • Statistics quantifies numbers
  • Machine learning generalizing information from large data sets
  • Principles to detect and extrapolate patterns
  • Machine Learning System Analysis and Design
  • Support Vector Machines

The Basics of Machine Learning

  • Data and Data Science
  • Machine Learning Techniques, Tools and Algorithms
  • Popular Machine Learning Methods
  • Supervised learning and unsupervised learning
  • Supervised learning algorithms and labeled data
  • Trained using labeled examples
  • Classification, regression, prediction and gradient boosting
  • Supervised learning and patterns
  • Predicting the values of the label on additional unlabeled data
  • Using historical data to predict likely future events
  • Unsupervised learning and unlabeled data
  • Unsupervised learning against data that has no historical labels
  • Semi supervised learning
  • Using both labeled and unlabeled data for training
  • Classification, regression and prediction
  • Reinforcement learning
  • Robotics, gaming and navigation
  • Discovery through trial and error
  • The agent (the learner or decision maker)
  • The environment (everything the agent interacts with)
  • Actions (what the agent can do)

Learning Applied to AI  

  • Application of Supervised versus Unsupervised Learning
  • Case Study: credit card transactions as fraudulent charges
  • Self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition
  • Face recognition

Principles of Supervised Algorithms

  • Classification and regression supervised learning problems
  • Clustering and association unsupervised learning problems
  • Algorithms used for supervised and unsupervised problems
  • Supervised Machine Learning as a majority of practical machine learning
  • Supervised learning problems grouping into regression and classification problems
  • Principles of “Classification”
  • Principles of “Regression”
  • Popular examples of supervised machine learning algorithms
  • Linear regression for regression problems
  • Random forest for classification and regression problems
  • Support vector machines for classification problems

Principles of Unsupervised Learning

  • The goal for unsupervised learning
  • Modeling the underlying structure or distribution in the data
  • Ways to learn more about the data
  • Algorithms to discover and present the interesting structure in the data
  • Unsupervised learning problems grouping into clustering and association problems
  • Principles of “Clustering”
  • Ways to discover the inherent groupings in the data
  • Principles of “Association”
  • Ways to discover rules that describe large portions of your data
  • Examples of unsupervised learning algorithms
  • K-means for clustering problems
  • Apriori algorithm for association rule learning problems
  • Semi-Supervised Machine Learning
  • Unlabeled data and a mixture of supervised and unsupervised techniques
  • Collecting and storing unlabeled data

Principles of Neural Networks

  • Neural Networks Representation
  • Principles behind neural networks and models
  • Neural Networks Learning
  • Backpropagation algorithm
  • Learn parameters for a neural network.
  • Implementing your own neural network for credit card fraud
  • Advice for Applying Machine Learning
  • Best practices for applying machine learning in practice
  • Best ways to evaluate performance of the learned models

Introduction to Deep Learning

  • Principles of Deep Learning
  • Artificial Neural Networks
  • TensorFlow
  • Learning complicated patterns in large amounts of data
  • Identifying objects in images and words in sounds
  • Automatic language translation
  • Medical diagnoses

Applying AI and Machine Learning

  • Applying AI and machine learning to IoT
  • Financial services
  • DoD
  • Government
  • Health care
  • Marketing and sales
  • Oil and gas
  • Renewable Energy
  • Transportation
  • DoD
  • Space Exploration

Overview of Algorithms

  • Associations and sequence discovery
  • Bayesian networks
  • Decision trees
  • Expectation maximization
  • Gaussian mixture models
  • Gradient boosting and bagging
  • Kernel density estimation
  • K-means clustering
  • Local search optimization techniques
  • Multivariate adaptive regression splines
  • Nearest-neighbor mapping
  • Neural networks
  • Principal component analysis
  • Random forests
  • Self-organizing maps
  • Sequential covering rule building
  • Singular value decomposition
  • Support vector machines

Overview of Tools and Processes

  • Comprehensive data quality and management
  • GUIs for building models and process flows
  • Interactive data exploration
  • Visualization of model results
  • Comparisons of different machine learning models
  • Identify the best machine learning models
  • Automated ensemble model evaluation
  • Repeatable and reliable results
  • Integrated, end-to-end platforms to automate data-to-decision process
  • Exploratory Data Analysis with R
  • Loading, querying and manipulating data in R
  • Cleaning raw data for modeling
  • Reducing dimensions with Principal Component Analysis
  • Identifying outliers in data
  • Working with Unstructured Data
  • Mining unstructured data
  • Building and evaluating association rules
  • Constructing recommendation engines
  • Machine learning with neural networks

Case Studies and Workshops

  • Autonomous Vehicle
  • Robotics: Vision Intelligence and Machine Learning
  • Robotics: Dynamics and Control
  • Locomotion Engineering
  • Kinematics and Mathematical Models
  • Cybersecurity
  • Deep Space Exploration
  • Working with Tensorflow
  • Creating computational graph
  • Applying Artificial Neural Networks (ANN) 

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