Getting Started with Artificial Intelligence & Machine Learning Training Logo

Getting Started with Artificial Intelligence & Machine Learning Training

Self-Paced, Live Online & Classroom Enterprise Training

Master Machine Learning fundamentals & ML programming with our Machine Learning training. Implement ML algorithms in your organization’s Machine Learning projects , this Artificial Intelligence and Machine Learning course will enable your team with the required skills to develop interesting Machine Learning models.

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Prof. Dr. James G. Shanahan
Program Architect

Prof. Dr. James G. Shanahan

Dr. James has spent the past 25 years developing and researching cutting-edge artificial intelligent systems. He has (co)founded several companies, advises high-tech startups and held appointments at AT&T (Executive Director of Research), Turn Inc., Xerox Research, Mitsubishi Research, and at Clairvoyance Corp. He teaches at UC Berkeley and has published six books, more than 50 research publications, and has over 20 patents in the areas of machine learning and information processing.

What is Artificial Intelligence Course about?

Learn Machine Learning principles with in-depth practical exposure to how projects are implemented at organizations in this Machine Learning course. You learn all about real-world applications of ML & the essentials of statistics and ML models with expert guidance from experienced industry mentors.


Our ML training includes Cloudlabs integration so you gain hands-on experience working with KNIME tool. Create supervised learning models using regression, random forest classification and Develop unsupervised learning models using k-means clustering and association rule learning. 


We have chosen an approach for this course, so learners not only get an overview of the Machine Learning concepts but can easily gain first hand AI & ML experience without the need of knowing Programming or Advance Mathematics, through the Popular and User-friendly KNIME tool, suitable even for Non-Technical Professionals.


Equip your team with the skills needed in Getting Started with building Machine Learning Applications for your Organization.

What are the objectives of Artificial Intelligence Course ?

At the end of this Machine Learning course, you will be able to:

  • Explain the key components of any ML model
  • Discuss machine learning algorithm types and develop an appreciation for their real-world applications
  • Recognize the statistical principles that forms the foundation of ML
  • Develop basic Supervised learning models
  • Develop basic Unsupervised learning models
  • Apply various methods for testing machine learning training models
  • Quickly start developing basic ML models while learning the underlying theory
  • Work on application development projects at your organization that employs machine learning 

Who is Artificial Intelligence Course for?

  • Managers, Tech Leads, IT Project Heads getting started to lead AI, ML Projects in their organization
  • Developers & Engineers getting started on AI/ML projects
  • Teams looking to build Interesting ML models aligned with their business.

What are the prerequisites for Artificial Intelligence Course?

  • Basic knowledge of Statistics is good to have.

Available Training Modes

Live Online Training

16 Hours

Classroom Training

2 Days

Course Outline Expand All

Expand All

  • Introduction to Artificial Intelligence
  • Applications, Industries, and growth
  • Data science and Machine Learning
  • Techniques used for AI
  • AI for Everything
  • Different methods used for AI
  • Tradition Methods & New Methods
  • AI Agents
  • Challenges in operating AI systems
  • Building your AI strategy and roadmap
  • Types of Learning
  • Phases in a ML Project (CRSIP-DM Methodology)
  • The three basic problems in ML (Regression, Classification, Clustering)
  • Bias Variance Trade off
  • Evaluating a ML Model
  • Demo of a Simple Linear Regression solution
  • Revisiting the ML concepts using this demo
  • Installation of KNIME
  • Creating KNIME Workspace
  • Ingesting data into KNIME environment
  • Basic data manipulations
  • Querying and Displaying data using KNIME
  • Visualization in KNIME
  • Understanding of Regression Problem Scenario
  • Math behind Linear Regression (Ordinary Least Squares)
  • Gradient Descent Algorithm
  • Types of Regression (Simple Linear, Multiple, Polynomial)
  • Evaluating a Regression Model (MSE, R Squared, Adj R Square)
  • Predicting using a Regression Model
  • Demo of Linear Regression using MS Excel
  • Revisiting concepts of Regression Math using this demo
  • Demo of Regression using KNIME (Boston Housing Prices Prediction)
  • Understanding and communicating the Regression Model to others
  • Real life applications of Regression
  • Understanding of Classification Problem Scenario
  • Introduction to Logistic Regression
  • Sigmoid function
  • Evaluating a Logistic Regression Model (Accuracy, Sensitivity)
  • Predicting using a Logistic Regression Model
  • KNIME Demo of Logistic Regression Model (Iris dataset)
  • Understanding Decision Trees
  • KNIME Demo of Decision Trees (Iris dataset)
  • Comparing models of Logistic Regression and Decision Trees
  • Understanding Random Forest
  • Real life applications of Classification
  • Significance of Unsupervised learning in ML
  • Concept of distance measure (Euclidean distance, Manhattan distance)
  • Introduction to Hierarchical Clustering
  • Introduction to K Means Clustering
  • Evaluating a Clustering output
  • Demo of Hierarchical Clustering using KNIME
  • Demo of K Means Clustering using KNIME
  • Comparison of clusters from Hierarchical and K Means Demos
  • Real life applications of Clustering
  • Issues in acquisition of data
  • Issues in data quality
  • Identifying and handling outliers
  • Importance missing data and techniques to handle
  • Understanding Normalization and standardization
  • Sensitivity of ML methods to distance and need for rescaling data
  • Handling Categorical Data (e.g. One Hot Encoding)
  • Feature Engineering
  • Curse of dimensionality, Dimensionality Reduction
  • Dimensionality Reduction, Data Compression
  • Concept and Mathematical modelling
  • Use Cases
  • Programming using Python
  • IRIS Data Analysis using PCA
  • Introduction to Reinforcement Learning
  • Exact Methods
  • Approximate Methods
  • Real life applications of Reinforcement Learning
  • What is Deep Learning?
  • Why Deep Learning?
  • Feature Extraction
  • Working of a Deep Network
  • Types of Deep Networks
  • Real life applications of Deep Learning
  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Introduction to Natural Language Processing
  • NLP Example using Keras library

Reviews

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