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:

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

 

 

Requirements

Graduated from any discipline

Course Project
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Used Tools
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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