BKIICT-BCC

Artificial Intelligence for Professionals

Course Overview/Objective

Course Objective: This course is designed to introduce professionals to the foundational and practical aspects of Artificial Intelligence (AI), covering essential techniques in machine learning, deep learning, natural language processing, and computer vision. It emphasizes hands-on experience using Python-based tools and frameworks to build and evaluate AI models for real-world applications across industries such as business, healthcare, and finance. Participants will develop a strong conceptual and practical grasp of AI, empowering them to design, implement, and evaluate AI-driven solutions in a responsible and ethical manner.

Learning Outcomes:

Define key concepts in Artificial Intelligence and distinguish AI from Machine Learning and Deep Learning.
Apply Python programming and essential libraries to process and analyze data for AI tasks.
Clean, transform, and visualize data for effective exploratory data analysis.
Build and evaluate basic machine learning models for supervised and unsupervised tasks using scikit-learn.
Understand the fundamentals of neural networks and develop simple deep learning models using TensorFlow or Keras.
Apply Natural Language Processing techniques for tasks like sentiment analysis and text classification.
Understand the principles of computer vision and implement simple CNN models for image recognition.
Explore real-world AI applications and use cases across different sectors.
Complete an AI capstone project, from problem definition to model development and presentation.
Understand ethical considerations and the impact of AI on society and industry.

Requirements

Graduated from any discipline

Course Project

Used Tools

Course Outline

Module 1: Introduction to Artificial Intelligence

What is AI? History and evolution
AI vs Machine Learning vs Deep Learning
Real-world applications in business, healthcare, finance, etc.
Ethical and social implications

Module 2: Foundations of AI & Python Basics

Python basics relevant to AI
Data structures, loops, functions, libraries
Introduction to Numpy, Pandas, Matplotlib
Hands-on: Writing basic AI scripts in Python

Module 3: Data Preparation & Exploratory Data Analysis

Data types, data cleaning, missing values
Data transformation and normalization
Visualization techniques
Hands-on: Exploratory analysis using Pandas/Matplotlib/Seaborn

Module 4: Machine Learning Fundamentals

Supervised Learning: Linear Regression, Decision Trees, Random Forests
Unsupervised Learning: K-means, Hierarchical Clustering
Model training, testing, evaluation metrics
Hands-on: Build ML models using scikit-learn

Module 5: Neural Networks & Deep Learning

Introduction to Artificial Neural Networks
Deep learning concepts (Activation functions, layers, epochs, etc.)
Frameworks: TensorFlow or Keras overview
Hands-on: Build a basic ANN for classification

Module 6: Natural Language Processing

Text preprocessing (Tokenization, stopwords, stemming)
Sentiment analysis
Word embeddings (Word2Vec, TF-IDF)
Hands-on: Sentiment analysis on real data

Module 7: Computer Vision

Image processing basics
Convolutional Neural Networks (CNNs) overview
Applications in object detection, facial recognition
Hands-on: Image classification using CNN (Keras)

Module 8: AI in the Real World

AI in Business Intelligence & Automation
AI in Healthcare, Finance, Marketing, and Cybersecurity
Case studies from industry
AI tools & platforms (ChatGPT, Azure AI, AWS Sagemaker)

Module 9: Capstone Project / Evaluation

Mini AI project: Choose a domain-specific problem
Model building, evaluation & presentation
Discussion on deployment basics

Incorporating feedback on your portfolio

Course Type :

Short Course

Course Duration :

2 Months

Course Hour(s) :

Classes :

Tuition Fee :

7500.00

Batch:

Reg Deadline:

01 Aug 2025

Class Shift:

Evening

Class Start:

01 Aug 2025

Cordinator