Artificial Intelligence (AI)

Course Overview:

• This track is designed for individuals who want to become Artificial Intelligence Engineers or Data Analytic Engineers.
• This comprehensive AI course provides a strong foundation in Python programming, mathematics, machine learning (ML), and deep learning (DL), equipping students with essential skills for AI-driven industries. As AI transforms sectors such as healthcare, finance, robotics, and automation, mastering these fundamentals is critical for future-proofing careers in data science, AI development, and research.
• Through hands-on projects (using NumPy, Pandas, TensorFlow, and scikit-learn), students will learn to preprocess data, build ML models, implement neural networks, and deploy AI solutions—bridging the gap between theory and real-world applications.

Learning Outcomes:

By the end of this course, students will be able to:

• Master Python for AI– Use NumPy, Pandas, and Matplotlib for data manipulation and visualization.
• Apply Math & Stats in AI– Leverage linear algebra, probability, and calculus for ML model development.
• Preprocess & Analyze Data– Handle missing data, normalize datasets, and apply feature engineering.
• Build & Train ML Models– Implement KNN, SVM, Decision Trees, Neural Networks, and Clustering (K-Means).
• Evaluate Model Performance – Measure accuracy using MSE, RMSE, precision, recall, and F1-score.
• Develop Deep Learning Models– Construct ANNs, CNNs, and RNNs using TensorFlow.
• Understand transformer architecture, as introduced in “Attention Is All You Need”

Prerequisites:

• Strong knowledge of Mathematics.
• Good command over programming languages.
• Good Analytical Skills.
• Ability to understand complex algorithms.
• Basic knowledge of Statistics and modeling.

Target Certification:

• Participants who pass the course exam will receive a Certificate of Completion from MIU.
• Upon completion of the program, trainees will receive an HCIA-AI Certificate from Huawei Academy, validating their knowledge of Artificial Intelligence.
• Trainees will also receive a free NVIDIA DLI Certificate for Fundamentals of Deep Learning.
• Optionally, two AWS Academy courses may be offered, allowing students to earn the following training badges:
  • Machine Learning Foundations – Training Badge
  • Generative AI Foundations – Training Badge

Who Should Attend:

• Students, graduates or professionals from any field (technical or non-technical) who want to understand or apply AI in their careers or research.

Contents:

• Programming Background
  • Python Programming Language.
  • NumPy (Numerical Python Library).
  • Pandas (Python Text and Files Handling Library).
• Math and Stat Background
  • Basics of Linear Algebra (Vectors, determinants, and Matrices).
  • Basics of Probability (Mean, Variance, Covariance, Correlation, Conditional Probabilities).
  • Basics of Statistics (Normal Distribution 1-D and N-D).
  • Basic of Calculus (Differentiation and Physical meaning of gradient).
  • Data Preprocessing (Handling Missed Data, Data Statistics, Labeling Categories, One Hot Labeling, Data Splitting, Data Normalization, Data Visualization, Import from CSV, Export Data files).
• Assignments:
  • Missing Data Handling.
  • Data Visualization Using MatPlot Python Library.
• Basic Course in Machine Learning (ML)
  • ML Essentials.
  • Introduction to Machine Learning (ML vs Rule Based).
  • Classification vs Regression in ML.
  • Feature Extraction and features engineering.
  • Dimensionality Reduction (Principle Component Analysis).
  • Feature Selection Techniques.
  • Introduction to Clustering Techniques (Unsupervised learning).
  • Performance Measures for ML Systems (classification metrics (Accuracy, Precision, Recall, F1 Score,).) and Errors (Mean Square Error , Root Mean Square Error , Mean Absolute percent errors )
• Basic Techniques in Machine Learning
  • K-Nearest Neighbors (KNN).
  • Linear Regression.
  • Logistic Regression.
  • Linear Classification Using Minimum Sum Squared Error.
  • Decision Trees.
  • Naive Bayes Classifier.
  • Support Vector Machine (SVM).
  • Neural Networks (NN).
  • K-Means Clustering Algorithm.
• Assignments and Practices on ML
  • (Used Data sets are IRIS data set and MNIST Dataset).
  • KNN Classifier (IRIS Data Set).
  • Linear Regression (1-D, 2-D) for any function with additional Random Noise.
  • Linear Classification using Minimum Sum squared Error.
  • Linear Classification using Iterative Algorithm.
  • Classification using Decision Tree.
  • Numeral Classification using SVM.
  • Build and Train Complete Neural Network from scratch.
• Basics of Deep Learning
  • Deep neural network development
  • Activation function
  • Optimization methods
  • Optimizer
  • Methods to prevent overfitting
  • Deep learning Implementation based on TensorFlow
  • Convolutional neural network (CNN)
  • Recurrent neural network (RNN)
• Transformer Architecture
• Workshop: Fundamentals of Deep Learning (Nvidia DLI)- 6 hours.

Recommended Next Course:

•  Deep Learning

JOB Profile:

• AI or Machine Learning Engineer (Entry to Mid-level)
• Deep Learning or Computer Vision Engineer
• AI Solutions Developer or AI Cloud Integration Specialist
• Cloud AI Engineer or AI Research Assistant

Work Environments:

• Tech and software companies
• AI research labs and R&D centers
• Cloud service and consulting companies
• Industries using AI such as healthcare, education, telecom, autonomous systems, and SaaS

Estimated Time to Completion: 76 Hours

Time Plan: Batch 1_July 2026

Day	Topics & Activities	No. Of Hours	Date	Time	Location
Day 1	Introduction to Artificial Intelligence: basic concepts	6 H	July 05, 2026		on campus
Day 2	Mathematics for Artificial Intelligence and Machine Learning	6 H	July 06, 2026		on campus
Day 3	Python fundamentals for data science: NumPy, Pandas, and Matplotlib	5 H	July 07, 2026		online
Day 4	Data Preprocessing with Python	5 H	July 08, 2026		online
Day 5	Linear Regression, Optimization Techniques, Regularization, and Evaluation of Regression Methods	6 H	July 12, 2026		on campus
Day 6	Logistic Regression, k-Nearest Neighbors, Naive Bayes, and Support Vector Machines	5 H	July 13, 2026		online
Day 7	Decision Trees, Ensemble Learning, and Clustering Methods	5 H	July 14, 2026		online
Day 8	Introduction to Neural Networks	5 H	July 15, 2026		online
Day 9	Neural Network Concepts: Optimizers, Regularization, Batch Normalization, Vanishing and Exploding Gradients	6 H	July 19, 2026		on campus
Day 10	Introduction to Convolutional Neural Networks	5 H	July 20, 2026		online
Day 11	Common CNN Architectures: VGG, ResNet, Inception, etc.	5 H	July 21, 2026		online
Day 12	Introduction to NLP, RNN, LSTM, GRU, and Transformer Architecture	5 H	July 22, 2026		online
Day 13	Practical Applications and Model Review	6 H	July 25, 2026		on campus
Day 14	Workshop: Fundamentals of Deep Learning — NVIDIA DLI	6 H	July 26, 2026		on campus

Time Plan: Batch 2_August 2026

Day	Topics & Activities	No. Of Hours	Date	Time	Location
Day 1	Introduction to Artificial Intelligence: basic concepts	6 H	August 16, 2026		on campus
Day 2	Mathematics for Artificial Intelligence and Machine Learning	6 H	August 17, 2026		on campus
Day 3	Python fundamentals for data science: NumPy, Pandas, and Matplotlib	5 H	August 18, 2026		online
Day 4	Data Preprocessing with Python	5 H	August 19, 2026		online
Day 5	Linear Regression, Optimization Techniques, Regularization, and Evaluation of Regression Methods	6 H	August 23, 2026		on campus
Day 6	Logistic Regression, k-Nearest Neighbors, Naive Bayes, and Support Vector Machines	5 H	August 24, 2026		online
Day 7	Decision Trees, Ensemble Learning, and Clustering Methods	5 H	August 25, 2026		online
Day 8	Introduction to Neural Networks	5 H	August 26, 2026		online
Day 9	Neural Network Concepts: Optimizers, Regularization, Batch Normalization, Vanishing and Exploding Gradients	6 H	August 30, 2026		on campus
Day 10	Introduction to Convolutional Neural Networks	5 H	August 31, 2026		online
Day 11	Common CNN Architectures: VGG, ResNet, Inception, etc.	5 H	September 01, 2026		online
Day 12	Introduction to NLP, RNN, LSTM, GRU, and Transformer Architecture	5 H	September 02, 2026		online
Day 13	Practical Applications and Model Review	6 H	September 03, 2026		on campus
Day 14	Workshop: Fundamentals of Deep Learning — NVIDIA DLI	6 H	September 04, 2026		on campus