An Artificial Intelligence Engineer Program is designed to provide a thorough education in various aspects of AI, preparing students for a successful career in the field. Here is an overview of the key components typically included in such a program:

1. Foundational Courses

These courses cover essential programming and mathematical concepts needed for AI. Topics include:

• Python for Data Science: Introduction to Python, basics, data structures, control flow statements, functions, object-oriented programming, file handling, and exception handling.
• Mathematics for ML & DL: Set theory, linear algebra, and calculus relevant to machine learning (ML) and deep learning (DL).
• Applied Statistics with Python: Descriptive statistics, probability, probability distributions, sampling techniques, and inferential statistics.
  
  

2. Data Science and Analysis

Focuses on acquiring, manipulating, and visualizing data to derive insights. Topics include:

• Data Science with Python: Mathematical computations with Numpy, data manipulation with Pandas, and visualization using matplotlib and seaborn.
• SQL Training: Database normalization, MySQL setup, working with tables, SQL functions, subqueries, window functions, views, stored procedures, and performance optimization.
  
  

3. Machine Learning and Predictive Modeling

Covers the theory and practical application of ML algorithms. Topics include:

• Machine Learning: Data wrangling, feature engineering, supervised learning (classification and regression), unsupervised learning, and ensemble learning.
• Deep Learning with Keras & TensorFlow: Artificial neural networks (ANNs), deep neural network tools, optimization, tuning, interpretability, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders.
  
  

4. Advanced Analytics

Delves into specialized techniques for complex AI problems. Topics include:

• Computer Vision with OpenCV: Image formation, CNN architectures, object detection, image segmentation, variational autoencoders (VAEs), neural style transfer, deep generative models, and optical character recognition (OCR).
• Natural Language Processing (NLP): Text data analysis, text vectorization, distributed representations, machine translation, sequence modeling, attention mechanisms, audio synthesis, and generative models for audio.
  
  

5. AIOps (AI in Production)

Focuses on deploying and managing AI systems. Topics include:

• Web Frameworks: Flask, FastAPI, Streamlit.
• Version Control and CI/CD: Git, Jenkins, GitHub Actions.
• Experiment Tracking and Monitoring: MLflow, Grafana.
• Containerization and Orchestration: Docker, Kubernetes.
  
  

6. Capstone Projects

Hands-on projects to apply and demonstrate skills acquired throughout the program. Projects include:

• Sentiment analysis on social media data
• Amazon product sentiment analysis
• Facial recognition for healthcare
• Automatic boat classification
• Spotify song recommendation system