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AI using data Science

Original price was: ₹1,000.00.Current price is: ₹500.00.

The primary objective of this program is to provide participants with a solid foundation in both data science and artificial intelligence, empowering them to design intelligent solutions driven by data insights. The course aims to develop proficiency in data handling, exploratory analysis, and model development while introducing learners to modern AI techniques such as machine learning and deep learning. Additionally, the program focuses on helping participants understand the end-to-end AI lifecycle—from data acquisition to model deployment—ensuring they can apply industry-standard tools and methodologies to real-world challenges.

Category: Analytics

Description

1. Learning Outcomes

Understand key concepts in AI and data science.
Clean, analyze, and interpret datasets.
Build and evaluate machine learning models.
Apply basic deep learning techniques.
Use tools like Python and TensorFlow.
Develop a simple AI-driven project.

2. Program Highlights & Key Features

Hands-on labs and real-world case studies.
Training with industry-standard tools.
Project-based learning with a final capstone.
Expert guidance and practical insights.
Coverage of latest trends in AI and data science.

Module 1: Introduction to AI and Data Science
Module 2: Data Preparation for AI
Module 3: Machine Learning Basics
Module 4: AI Techniques Using Data Science
Module 5: Tools, Ethics, and Applications

Introduction to AI and Data Science

What is Artificial Intelligence
What is Data Science
Relationship Between AI and Data Science
Types of AI (Narrow AI, General AI – Overview)
Role of Data in AI Systems
Real-World Applications of AI
Conclusion and QnA

Data Preparation for AI

Data Collection and Understanding
Structured vs Unstructured Data
Data Cleaning and Preprocessing
Feature Selection and Feature Engineering (Basics)
Handling Missing and Noisy Data
Importance of Data Quality in AI
Conclusion and QnA

Machine Learning Basics

Introduction to Machine Learning
Supervised vs Unsupervised Learning
Classification and Regression (Conceptual)
Clustering Basics
Model Training and Testing
Model Evaluation Basics
Conclusion and QnA

AI Techniques Using Data Science

Predictive Analytics Using Data
Pattern Recognition and Insights
Recommendation Systems (Conceptual)
Time-Based Prediction (Conceptual)
AI Use Cases in Business
Limitations of AI Models
Conclusion and QnA

Tools, Ethics, and Applications

Tools Used for AI and Data Science (Python, R – Overview)
Role of Visualization in AI Insights
Ethical Use of AI
Bias and Fairness in AI Models
AI in Finance, Marketing, HR, and Healthcare
Future Scope of AI Using Data Science
Conclusion and QnA

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