AI Fundamentals

History and Evolution of AI: Tracing the origins and key milestones in AI development.
Introduction to Machine Learning: Core ML concepts, how ML differs from traditional programming.
Types of Machine Learning: Overview of supervised, unsupervised, and reinforcement learning.

Data Cleaning Techniques: Handling missing data, noise, and outliers. 

Data Visualization: Using tools like Matplotlib and Seaborn for insights. 

Feature Engineering: Techniques for extracting and selecting features. 

Regression Techniques: Linear and logistic regression. 

Classification Methods: Decision trees, Support Vector Machines (SVMs). 

Model Evaluation: Accuracy, precision, recall, and other metrics. 

Clustering Algorithms: K-means clustering and its applications. 

Dimensionality Reduction: Principal Component Analysis (PCA) and its use cases. 

Advanced Clustering Techniques: Hierarchical and density-based clustering. 

Fundamentals of Neural Networks: Basic concepts, architecture, and functioning. 

Convolutional Neural Networks (CNNs): Application in image processing. 

Recurrent Neural Networks (RNNs): Use in sequence data analysis. 

Model Performance Metrics: Understanding different metrics for evaluating models. 

Hyperparameter Tuning: Techniques like grid search and random search. 

Overfitting and Underfitting: Strategies to address these common issues. 

Ethical Issues in AI: Understanding the ethical implications of AI technologies. 

Bias in Machine Learning: Identifying and mitigating biases in ML models. 

Responsible AI: Best practices for ethical AI development and deployment.