Introduction to Artificial Intelligence

• Overview of AI: Definitions, scope, and applications

• Key historical milestones in AI development

• Types of AI: Narrow AI vs. General AI

• AI in real-world industries: Healthcare, finance, automotive, and more

Foundations of AI and Machine Learning

• Understanding machine learning vs. deep learning

• The AI lifecycle: Data collection, preprocessing, model training, and evaluation

• Overview of key algorithms: Supervised learning, unsupervised learning, and reinforcement learning

• Introduction to neural networks and deep learning

Data Science Fundamentals for AI

• The role of data in AI development

• Data types, structures, and preprocessing techniques

• Data cleaning and feature engineering

• Introduction to data visualization and statistical analysis

Key AI Techniques and Algorithms

• Linear regression and logistic regression

• Decision trees and random forests

• Support vector machines (SVM)

• K-means clustering and principal component analysis (PCA)

• Introduction to natural language processing (NLP) and computer vision

Deep Learning and Neural Networks

• Introduction to deep neural networks (DNN)

• Understanding the architecture: Layers, activation functions, and loss functions

• Convolutional neural networks (CNNs) for image recognition

• Recurrent neural networks (RNNs) and LSTMs for sequence prediction

• Training deep learning models using TensorFlow and Keras

AI in Practice: Building and Deploying Models

• Model evaluation: Accuracy, precision, recall, F1-score, and AUC

• Overfitting, underfitting, and bias-variance tradeoff

• Hyperparameter tuning and optimization techniques

• Deploying AI models: APIs, cloud-based solutions, and edge computing

Advanced AI Topics

• Reinforcement learning and Q-learning

• Generative adversarial networks (GANs)

• AI ethics: Bias in AI, explainability, and fairness

• Future trends in AI: Autonomous systems, robotics, and AI-driven innovation

Hands-On Practical Sessions

• Building AI models using Python and popular libraries: TensorFlow, Keras, PyTorch

• Hands-on coding exercises for supervised and unsupervised learning models

• Implementing neural networks for classification and regression tasks

• Working on real-world datasets to practice building, evaluating, and optimizing AI models

AI Applications and Case Studies

• AI applications in various industries: Healthcare, retail, automotive, and entertainment

• Real-world case studies: AI in autonomous vehicles, recommendation systems, and fraud detection

• Exploring successful AI projects and lessons learned from failures

Assessment and Review

• Knowledge checks, quizzes, and coding challenges

• Peer reviews and group discussions on AI project work

• Trainer feedback on project improvement and best practices

Post-Course Support

• Access to course materials, code repositories, and project resources

• Continuous trainer support via email, community forums, or Slack/Telegram groups

• Resources for further learning: Books, papers, and MOOCs

Career Guidance and Certification Path

• AI certifications and career opportunities in AI/ML roles

• How AI skills can enhance your career in data science, software development, and business intelligence

• Next steps for advanced AI learning: Deep learning, computer vision, NLP, and more