Applied Artificial Intelligence and Machine Learning

Duration: 12 months

About this program

Artificial Intelligence (AI) and Machine Learning (ML) are among the most sought after and highly compensated digital economy skills. In the past decade, AI has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. AI is so pervasive that we use it dozens of times a day without even realizing it. Many researchers also think it is the best way to make progress towards human-level AI.

The Applied Artificial Intelligence and Machine Learning Bootcamp is a 12-month intensive program for leaders who want to use the knowledge of data and AI to make informed strategic business decisions. The program is suitable for non-tech business professionals who are looking to leverage the power of AI in their day-to-day decision-making.

KEY LEARNING OUTCOMES

  1. Build your expertise in the most widely-used AI & ML  tools and technologies.
  2. Acquire the ability to independently solve business problems using AI & ML.
  3. Master the skills needed to build Machine Learning and Deep Learning models.
  1. Develop know-how of the applications of AI in areas such as Computer Vision and NLP.
  1. Understand the possibilities and implications of AI in different industries.
  1. Build a substantial body of work and an industry-ready portfolio in AI & ML.

PROGRAM BENEFITS

  1. Mentored learning sessions with industry experts.
  2. Real-world case studies to build industry context.
  3. Projects that don’t require coding experience.
  4. Domain understanding coupled with technical coverage.
  5. Capstone Project to consolidate your learning.
  6. Certificate of Completion from New Vision Institute of Technology.

Course Outline for Applied Artificial Intelligence and Machine Learning

  1. Business of AI
  • Introduction To Artificial Intelligence
  • Explosion In AI
  • Business Application And Its Limitations
  • Building AI Project
  • ROI Calculation
  • Case Study
  1. Data Visualization Using KNIME
  • What Is Data?
  • Numerical And Textual Data
  • Graphs & Networks
  • Time Series Data
  • Di erent Types Of Data Objects
  • Understanding Visual Metrics:
  • Mean, Median & Mode
  • Introduction to KNIME
  • Visualizing Data Using KNIME
  • Data Manipulation Using KNIME
  1. Regression
  • Introduction To Regression
  • Linear Regression
  • Multivariate Linear Regression
  • Categorical Independent Variable In
  • Regression
  • Root Mean Square Error And Mean Absolute Error
  • Linear Regression – Pros & Cons
  • Hands-on Using KNIME
  • Case Study Session With Experts
  1. Classification
  • Introduction To Classification
  • Logistic Regression
  • Setting Up Threshold
  • Performance Measures – Precision & Recall
  • Evaluation Of Models
  • Hands-on Using KNIME
  • Case Study Session With Experts
  1. Building POC for AI Projects
  • Building POC – Outline
  • Solution At A Glance
  • Market Potential
  • Threats & Opportunities
  • Requirements – Data & People
  • Product Development Roadmap
  • Expansion Plan
  • AI Techniques & Their Relevance To Domains
  • Identifying AI Use Cases
  • Tips For Building Successful AI Product
  1. Neural Networks
  • Introduction To Neural Networks
  • Activation Function
  • Feed Forward Neural Network
  • Topology Of A Neural Network
  • Error & Loss Function
  • Training A Neural Network
  • Optimizing A Neural Network
  • Hands-on Using KNIME
  1. Ensemble Techniques
  • Introduction To Decision Trees
  • Cart
  • Pruning
  • Ensemble Techniques
  • Random Forest
  • Hands-on Using KNIME
  • Case Study Session With Experts
  1. Clustering & Dimensionality Reduction
  • Introduction To Clustering
  • Types Of Clustering
  • K-Means Clustering
  • Importance Of Scaling
  • Applications Of Clustering
  • Advantages & Disadvantages Of Clustering
  • Visual Analysis
  • Hands-on Using KNIME
  1. Building AI Teams & Driving Data Culture
  • Service Vs Product Companies
  • AI Team Composition
  • Centralized Vs Distributed AI Teams
  • How To Keep Your Team Motivated?
  • Handling Resistance From Senior Management
  • Coaching Others
  • Managing Portfolio Of Projects
  • Scaling AI Teams
  1. Recommendation Systems
  • Introduction To Recommendation Systems
  • Content Based Filtering
  • Collaborative Filtering
  • Similarity Measures
  • Case Study
  • Hybrid Systems
  • Hands-on Using KNIME
  • Case Study Session With Experts
  1. Natural Language Processing (NLP)
  • Introduction To NLP
  • Different Tasks In NLP
  • How Are NLP Problems Solved?
  • Text Extraction/Web Scraping
  • Building A Model
  • Case Study – Sentiment Analysis
  • NLP Demonstration On Sentiment Analysis
  • Hands-on Using AWS
  • Case Study Session With Experts
  1. Computer Vision (CV)
  • Introduction To Computer Vision
  • Types Of CV Problems
  • Pixel
  • How Does The Computer See An Image?
  • 3D Images
  • Resolution
  • Convolution & Pooling
  • Convolutional Neural Networks
  • Hands-On Using AWS
  • Case Study Session With Experts
  1. Jumpstarting AI
  • Transfer Learning
  • How It Works
  • Applications Of Transfer Learning –
  • Advantages Vs Disadvantages
  • Dealing With Imbalanced Data –
  • Data Augmentation
  • Data Augmentation Types
  • Model Deployment
  • Modes Of Training
  • Serialization
  • Model Monitoring And Recalibration