Most popular
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Beginner
Machine Learning and Artificial Intelligence in Business – Driving Business Innovation with AI and Machine Learning
₹1,499.00₹3,000.00
Machine Learning and Artificial Intelligence in Business – Driving Business Innovation with AI and Machine Learning
What you'll learn
Week 1: Introduction to AI and Machine Learning (4 Hours)
Session 1 (2 Hours): Introduction to AI and Machine Learning in Business
Overview of AI and Machine Learning
Historical Context and Evolution of AI
Key Concepts and Terminology
Session 2 (2 Hours): Understanding Machine Learning Models
Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Introduction to Algorithms and Model Selection
Basic Tools and Software Overview (e.g., Python, TensorFlow)
Week 2: Data Management and Preprocessing (6 Hours)
Session 3 (2 Hours): Data Collection and Management
Understanding Data Types and Sources
Data Collection and Storage Strategies
Ethics and Privacy in Data Handling
Session 4 (2 Hours): Data Preprocessing and Feature Engineering
Data Cleaning and Transformation
Feature Selection and Engineering Techniques
Introduction to Data Visualization Tools
Session 5 (2 Hours): Exploratory Data Analysis (EDA)
Techniques for EDA
Identifying Patterns and Anomalies in Data
Using EDA Tools and Libraries
Week 3: Building and Evaluating Models (6 Hours)
Session 6 (2 Hours): Supervised Learning Techniques
Introduction to Regression and Classification Models
Building and Training Models
Case Studies in Business Applications
Session 7 (2 Hours): Unsupervised Learning and Clustering
Overview of Clustering Techniques
Dimensionality Reduction Methods
Practical Applications in Market Segmentation
Session 8 (2 Hours): Model Evaluation and Tuning
Techniques for Evaluating Model Performance
Overfitting, Underfitting, and Model Tuning
Cross-Validation and Hyperparameter Tuning
Week 4: Advanced Topics and Business Applications (4 Hours)
Session 9 (2 Hours): Advanced Topics in AI and ML
Introduction to Neural Networks and Deep Learning
AI in Natural Language Processing and Computer Vision
Emerging Trends and Future of AI in Business
Session 10 (2 Hours): Strategic Implementation and Capstone Project
Strategies for Implementing AI and ML in Business
Ethical Considerations and AI Governance
Capstone Project Presentation and Course Wrap-Up
The course would ideally blend lectures with hands-on exercises, case studies, and project work. The capstone project should involve applying AI and ML concepts to a real-world business problem, encouraging students to think critically about the strategic application of these technologies in a corporate setting. This structure ensures that MBA students gain not only the technical knowledge of AI and ML but also understand how to leverage these technologies for strategic advantage in various business sectors.
Trending
-50%
Beginner
Machine Learning and Artificial Intelligence in Business – Driving Business Innovation with AI and Machine Learning
₹1,499.00₹3,000.00
Machine Learning and Artificial Intelligence in Business – Driving Business Innovation with AI and Machine Learning
What you'll learn
Week 1: Introduction to AI and Machine Learning (4 Hours)
Session 1 (2 Hours): Introduction to AI and Machine Learning in Business
Overview of AI and Machine Learning
Historical Context and Evolution of AI
Key Concepts and Terminology
Session 2 (2 Hours): Understanding Machine Learning Models
Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Introduction to Algorithms and Model Selection
Basic Tools and Software Overview (e.g., Python, TensorFlow)
Week 2: Data Management and Preprocessing (6 Hours)
Session 3 (2 Hours): Data Collection and Management
Understanding Data Types and Sources
Data Collection and Storage Strategies
Ethics and Privacy in Data Handling
Session 4 (2 Hours): Data Preprocessing and Feature Engineering
Data Cleaning and Transformation
Feature Selection and Engineering Techniques
Introduction to Data Visualization Tools
Session 5 (2 Hours): Exploratory Data Analysis (EDA)
Techniques for EDA
Identifying Patterns and Anomalies in Data
Using EDA Tools and Libraries
Week 3: Building and Evaluating Models (6 Hours)
Session 6 (2 Hours): Supervised Learning Techniques
Introduction to Regression and Classification Models
Building and Training Models
Case Studies in Business Applications
Session 7 (2 Hours): Unsupervised Learning and Clustering
Overview of Clustering Techniques
Dimensionality Reduction Methods
Practical Applications in Market Segmentation
Session 8 (2 Hours): Model Evaluation and Tuning
Techniques for Evaluating Model Performance
Overfitting, Underfitting, and Model Tuning
Cross-Validation and Hyperparameter Tuning
Week 4: Advanced Topics and Business Applications (4 Hours)
Session 9 (2 Hours): Advanced Topics in AI and ML
Introduction to Neural Networks and Deep Learning
AI in Natural Language Processing and Computer Vision
Emerging Trends and Future of AI in Business
Session 10 (2 Hours): Strategic Implementation and Capstone Project
Strategies for Implementing AI and ML in Business
Ethical Considerations and AI Governance
Capstone Project Presentation and Course Wrap-Up
The course would ideally blend lectures with hands-on exercises, case studies, and project work. The capstone project should involve applying AI and ML concepts to a real-world business problem, encouraging students to think critically about the strategic application of these technologies in a corporate setting. This structure ensures that MBA students gain not only the technical knowledge of AI and ML but also understand how to leverage these technologies for strategic advantage in various business sectors.
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Machine Learning and Artificial Intelligence in Business – Driving Business Innovation with AI and Machine Learning
“Machine Learning and Artificial Intelligence in Business – Driving Business …
What you'll learn
Week 1: Introduction to AI and Machine Learning (4 Hours)
Session 1 (2 Hours): Introduction to AI and Machine Learning in Business
Overview of AI and Machine Learning
Historical Context and Evolution of AI
Key Concepts and Terminology
Session 2 (2 Hours): Understanding Machine Learning Models
Types of Machine Learning: Supervised, Unsupervised, Reinforcement
Introduction to Algorithms and Model Selection
Basic Tools and Software Overview (e.g., Python, TensorFlow)
Week 2: Data Management and Preprocessing (6 Hours)
Session 3 (2 Hours): Data Collection and Management
Understanding Data Types and Sources
Data Collection and Storage Strategies
Ethics and Privacy in Data Handling
Session 4 (2 Hours): Data Preprocessing and Feature Engineering
Data Cleaning and Transformation
Feature Selection and Engineering Techniques
Introduction to Data Visualization Tools
Session 5 (2 Hours): Exploratory Data Analysis (EDA)
Techniques for EDA
Identifying Patterns and Anomalies in Data
Using EDA Tools and Libraries
Week 3: Building and Evaluating Models (6 Hours)
Session 6 (2 Hours): Supervised Learning Techniques
Introduction to Regression and Classification Models
Building and Training Models
Case Studies in Business Applications
Session 7 (2 Hours): Unsupervised Learning and Clustering
Overview of Clustering Techniques
Dimensionality Reduction Methods
Practical Applications in Market Segmentation
Session 8 (2 Hours): Model Evaluation and Tuning
Techniques for Evaluating Model Performance
Overfitting, Underfitting, and Model Tuning
Cross-Validation and Hyperparameter Tuning
Week 4: Advanced Topics and Business Applications (4 Hours)
Session 9 (2 Hours): Advanced Topics in AI and ML
Introduction to Neural Networks and Deep Learning
AI in Natural Language Processing and Computer Vision
Emerging Trends and Future of AI in Business
Session 10 (2 Hours): Strategic Implementation and Capstone Project
Strategies for Implementing AI and ML in Business
Ethical Considerations and AI Governance
Capstone Project Presentation and Course Wrap-Up
The course would ideally blend lectures with hands-on exercises, case studies, and project work. The capstone project should involve applying AI and ML concepts to a real-world business problem, encouraging students to think critically about the strategic application of these technologies in a corporate setting. This structure ensures that MBA students gain not only the technical knowledge of AI and ML but also understand how to leverage these technologies for strategic advantage in various business sectors.
