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Alpha Cybertech

Academic Programs

Artificial Intelligence (AI) is the simulation of human intelligence in machines that can think, learn, and make decisions. Machine Learning (ML) is a subset of AI that enables systems to learn patterns from data and improve over time. AI/ML is used in speech recognition, recommendation systems, self-driving cars, finance, healthcare, and more. Learning AI/ML involves understanding math, statistics, programming, and real-world problem-solving. Popular tools include Python, Scikit-learn, TensorFlow, and PyTorch.

Design intelligent systems that learn, adapt, and scale—become an AI/ML Architect

Artificial Intelligence & Machine Learning

Module 1

Introduction to AI & ML

  • What is Artificial Intelligence?
  • What is Machine Learning?
  • Differences between AI, ML, and Deep Learning
  • Real-world applications
Module 2

Basics of Python for AI/ML

  • Python installation & setup
  • Variables, Data Types, Loops, Functions
  • Libraries: NumPy, Pandas, Matplotlib
  • Jupyter Notebook usage
Module 3

Mathematics for ML

  • Linear Algebra Basics (Vectors, Matrices)
  • Probability & Statistics
  • Mean, Median, Standard Deviation
  • Data Normalization and Scaling
Module 4

Data Handling and Preprocessing

  • Collecting and cleaning datasets
  • Handling missing data
  • Encoding categorical variables
  • Feature scaling and selection
Module 5

Supervised Learning

  • What is supervised learning?
  • Algorithms: Linear Regression, Logistic Regression
  • Decision Trees, Random Forest, K-Nearest Neighbors
  • Model evaluation: Accuracy, Precision, Recall
Module 6

Unsupervised Learning

  • Clustering: K-Means, Hierarchical Clustering
  • Dimensionality Reduction: PCA
  • Applications: Customer segmentation, Anomaly detection
Module 7

Model Training and Evaluation

  • Train-test split
  • Cross-validation
  • Overfitting vs Underfitting
  • Confusion matrix and ROC-AUC
Module 8

Deep Learning Basics

  • Introduction to Neural Networks
  • Activation Functions
  • Building a simple neural network with TensorFlow/Keras
  • Overfitting control with Dropout
Module 9

Natural Language Processing (NLP)

  • Text preprocessing
  • Sentiment analysis
  • Bag of Words, TF-IDF
  • Intro to Transformers (optional)
Module 10

AI in Real-World Applications

  • AI in healthcare, finance, and retail
  • Ethics in AI
  • Bias and fairness in models
  • Case Studies and Projects
Module 11

Capstone Project

  • Choose a real dataset
  • Define problem, clean data, train model
  • Present results
  • Get feedback and refine