Unlock the potential of AI and ML to create innovative solutions that transform industries.

Learn the importance of data quality and preprocessing techniques.

• Data Collection and Cleaning
• Feature Engineering and Selection
• Splitting Data for Training and Testing

Dive into supervised learning algorithms.

• Regression Analysis (Linear and Logistic Regression)
• Decision Trees and Random Forests
• Support Vector Machines

Understand the techniques for unsupervised learning.

• Clustering Algorithms (K-means, Hierarchical Clustering)
• Dimensionality Reduction (PCA)
• Anomaly Detection

Explore the basics of neural networks and deep learning.

• Understanding Perceptrons and Feedforward Networks
• Activation Functions and Backpropagation
• Introduction to Convolutional Neural Networks (CNNs)

Learn how to evaluate and optimize machine learning models.

• Performance Metrics (Accuracy, Precision, Recall)
• Hyperparameter Tuning Techniques (Grid Search, Random Search)
• Cross-Validation Techniques

Explore how AI is applied in various domains.

• Natural Language Processing (NLP)
• Computer Vision Applications
• AI in Healthcare, Finance, and Marketing