Word count: 3500 words
Objectives to cover:
Introduction to AI/ML Model Development: Overview of the process and significance of building AI and ML models.
Supervised vs. Unsupervised Learning: Understanding the fundamental differences and use cases of both learning types.
Neural Networks: Exploring the structure and functioning of neural networks in pattern recognition and learning.
Decision Trees and Random Forests: Examining tree-based algorithms for classification and regression tasks.
Support Vector Machines: Analyzing how SVMs are used for high-dimensional data classification and separation.
Model Evaluation Metrics: Introduction to key metrics like accuracy, precision, recall, and their importance in model assessment.
Hyperparameter Tuning: Techniques for optimizing model performance through fine-tuning algorithm parameters.
Feature Engineering and Preprocessing: Enhancing model input through effective data cleaning and feature selection.
Challenges and Solutions in Model Development: Addressing common hurdles in AI/ML development and strategies to overcome them.
Reference: IEEE style