Artificial Intelligence (AI) and Data Science are revolutionizing how businesses operate, make decisions, and solve problems. AI enables machines to learn from data and perform tasks intelligently, while Data Science extracts valuable insights from vast datasets. Together, they power innovations in healthcare, finance, retail, and beyond. From predictive analytics to smart assistants, their applications are limitless. As data continues to grow exponentially, the demand for AI and Data Science professionals is also rising. Embracing these technologies is no longer optional—it’s essential for staying competitive in the digital age.
Outline
Module 1: Foundations of AI & Data Handling
• Introduction to Artificial Intelligence
• Concepts, Origins, and Evolution
• The Rise of AI: Modern Applications and Its Impact on Society
• Ethics in AI: Responsible Use, Transparency, and Governance
• Addressing Ethical Dilemmas: Bias, Fairness, and Accountability
• Regulatory, Legal, and Social Considerations of AI Deployment
• Preparing Data for AI: Cleaning, Transformation, and Standardization
• Exploratory Data Analysis (EDA): Uncovering Patterns and Insights
Module 2: Core Principles of Machine Learning
• Understanding Machine Learning: Key Concepts and Definitions
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• The Machine Learning Lifecycle: From Data Ingestion to Model Deployment
• Programming with Python: Essential Libraries for ML (NumPy, Pandas, Scikit-learn)
• Predictive Modeling: Introduction to Linear and Logistic Regression
• Evaluating Model Performance: Accuracy, Precision, Recall, and Beyond
• Practical Exercise: Build and Evaluate a Simple Regression Model
• Fundamentals of Unsupervised Learning: When Labels Aren’t Available
• Clustering Algorithms: K-Means, Hierarchical Clustering, and DBSCAN
• Reducing Complexity: Dimensionality Reduction with PCA
• Introduction to Recommender Systems: Personalized User Experience
• Detecting Anomalies: Outlier Detection Methods in ML
• Hands-on Lab: Apply K-Means Clustering to Real-World Datasets
Module 4: Neural Networks and Deep Learning Essentials
• Neural Networks Demystified: Architecture, Neurons, and Activation Functions
• Learning Through Layers: Backpropagation and Optimization
• Deep Learning in Practice: Convolutional Neural Networks (CNNs)
• Vision Systems: Image Recognition and Classification
• Leveraging Pretrained Models: Introduction to Transfer Learning
• Hands-on Lab: Design and Train a CNN for Image Classification
Module 5: Natural Language Processing (NLP)
• Introduction to NLP: Bridging Human Language and Machines
• Text Preparation: Tokenization, Normalization, and Noise Removal
• Feature Representation: Bag-of-Words and TF-IDF Techniques
• Understanding Sentiment and Classifying Text Data
• Word Embedding Models: Word2Vec, GloVe, and Vector Representations
• Advanced NLP: Sequence-to-Sequence Models and Named Entity Recognition (NER)
• Hands-on Lab: Build a Sentiment Analysis Application
Module 6: Advanced AI Concepts & Final Projects
• Decision Making with MDPs and Q-Learning
• Introduction to Deep Reinforcement Learning
• AI in the Real World: Case Studies in Healthcare, Finance, and Autonomous Systems
• The Future of AI: Innovations, Challenges, and Research Directions
• Capstone Project: Final Presentations and Demonstrations of Student Work
Course Fee
● Online Rs. 6,000/- Total
- Once paid, the fee is non-refundable and non-transferable
Account Details
Bank: Habib Bank Limited
Account Title: AIN GenX
Account No: 5910-70000512-03
IBAN No: PK08 HABB 0059 1070 0005 1203