Description
Module 1: Introduction to Machine Learning
– Basics of machine learning.
– Types of machine learning (e.g., supervised, unsupervised).
– Machine learning applications.
Module 2: Supervised Learning
– Regression and classification algorithms.
– Model training and evaluation.
– Feature engineering.
Module 3: Unsupervised Learning
– Clustering and dimensionality reduction.
– K-means clustering, PCA, and more.
– Anomaly detection.
Module 4: Deep Learning and Neural Networks
– Neural network fundamentals.
– Building and training deep learning models (e.g., TensorFlow, Keras).
– Convolutional and recurrent neural networks.
Module 5: Machine Learning in Practice
– Real-world machine learning projects.
– Model deployment and integration.
– Model monitoring and maintenance.
