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Certificate in Artificial Intelligence

Course Duration: 3.5 Months

Embark On an Odyssey Through The Digital Mindscape

Discover our Artificial Intelligence course, where imagination meets innovation. Journey into the heart of AI’s algorithms, neural networks, and cognitive computing, uncovering the secrets of machine learning and its endless possibilities. From autonomous vehicles to personalized medicine, explore how AI is revolutionizing every facet of our lives. Engage in hands-on experimentation with state-of-the-art tools and datasets, and delve into the ethical dimensions of AI in society. Whether you’re a visionary technologist, creative thinker, or problem-solving maverick, this course will empower you to shape the future with intelligence and ingenuity.
ICE School offers Diploma in Fashion and Marketing in Nepal

Why Learn AI & ML
In 2024?

By the end of 2024, AI is forecasted to create 2.3 Million jobs, with the highest demand for roles such as AI developers, machine learning engineers & data scientists. Also, according to studies done by the World Economic Forum(WEF), domains such as Artificial Intelligence, Machine Learning, and Data Analytics could create 133 Million new jobs globally. The average salary earned by a professional AI & ML Engineer is ₹11.3 Lakhs per Annum in India.
  • High Salary
  • High Job Demand
  • Wide Scope of AI & ML in India

Benefits of Our Program

Learn for industry experts
Live classes
Hands on workshops and practical classes
Technical support available in Nepali and English

Technologies Covered
In This Prorogram

Python
SQL
Machine Learning
Data Visualization
Deep Learning
Natural language Processing
Date Cleaning
Computer Vision
Artificial Intelligence

Curriculum

In this program, we adopt a case study methodology to disseminate the latest Developments in Cloud Technologies, Deep Learning, NLP and Machine Learning Model Building and its Deployment with the fundamentals of Artificial Intelligence.

Course Structure

Module 1: Introduction to AI and Computer vision
  • Introdution to AI and computer vision
  • What is AI in computer vision
  • AI application in the real world
  • Identity AI in daily life
Module 2: Components of Computer vision
  • Component of computer vision
  • Component of computer vision learning
  • Constructive computer vision model using AI tools
Module 3: Overview of Robotics in AI
  • Machine Learning in ANNs
  • Aspects of Robotics
  • Robot Locomotion
  • Components of a Robot
  • Applications of Robotics
Module 4: Implementing AI and LLMs in Business
  • Implementing AI and llMS in business analytics
  • Executing aristocratic connections with AI LLMS in business analytics
Module 5: Initiation to Large Language Models (LLMs)
  • Initiation to large language models
  • Introduction to ChatGPT 4
  • Requisition to human text generation conviction analysis
Module 6: AI Ethics in LLMs
  • Significance use of ethical considerations in AI
  • Virtuos challenges associated with Chat gpt 4 and LLMs
Module 7: Stream Project
  • Students initaites thier work on a project that involves applying AI, LLMs, or ChatGPT to solve a real-world problem
  • Project presentation and discussion
Module 8: ChatGPT and Limitations of AI – I
  • Initiation to ChatGPT and its limitations
  • Conversational AI and its applications in customer service, education, and content creation
Module 9: ChatGPT and Limitations of AI – II
  • Human Text Generation : Understanding human text generation and its Impacts
  • Applications: text summarization, language translation, and content generation
Module 10: AI Applications: Computer Vision
  • AI application computer vision
  • Applications: self driving car with object detection
Module 11: Natural Language Processing
  • Introduction to NLP
  • Components of NLP
  • NLP Terminology
  • Steps in Natural Language Processing
Module 12: Neural Networks
  • Artificial Neural Networks
  • What is a Neural Network?
  • Types of Artificial Neural Networks
  • Working of Artificial Neural Networks
Module 13: Natural Language Processing with Neural Networks continued

After having the basic understanding of deep learning architecture for language models, we will now look into more complex architectures.

  • Self Attention Networks: Transformers
  • Introduction to Encoder-Decoder models
  • Encoder-Decoder with RNNs
  • Attention and Beam search
  • Encoder and Decoder with Transformers
  • Transfer Learning through Fine-Tuning
Module 14: Introduction to SQL

We will dive into the SQL-based databases. We will understand the problems with file-based systems and how databases can overcome those challenges. We will learn the basics of SQL queries, schemas, and normalization.

  • Data Modeling
  • Normalization, and Star Schema
  • ACID transactions
  • Select, insert, update & delete (DML and DQL)
  • Join operations
  • Window functions (rank, dense rank, row number etc)
  • Data Types, Variables and Constants
  • Conditional Structures (IF, CASE, GOTO and NULL)
  • Integrating python with SQL
Module 15: Machine Learning Continued
  • Classification
  • Logistic regression
  • K nearest neighbours
  • Clustering
  • K means
  • Dimensionality reduction methods
  • Principal component analysis and its variants
  • Linear Discriminant Analysis
  • Support vector machine
Module 16: Machine Learning Refresher

We will cover the basics of Machine Learning and connect the use cases in the domain of Machine Learning with the Artificial Intelligence.

  • What is ML and how it is related to AI?
  • Predictive Modelling
  • Correlation
  • Basics of regression
  • Ordinary least squares
  • Simple linear regression
  • Model building
  • Model assessment and improvement
  • Diagnostics
  • Multiple linear regression (model building and assessment)
  • Random forest & decision tree

Join us for a practical Artificial Intelligence Course that prepares you for a dynamic career in the industry.

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