Machine Learning

Machine Learning (ML) has emerged as an exciting field of research within computer science and engineering. It is a subset of Artificial Intelligence (AI) that enables computers to extract meaningful patterns from data, akin to human intelligence. In this essay, well delve into the concepts, methods, and steps involved in ML.

At its core, ML involves teaching a computer system how to make accurate predictions based on data. These predictions can range from identifying fruits in photos to recognizing speech for video captions. The key distinction between traditional software and ML lies in the approach: rather than explicitly coding rules, ML models learn from data.

Introduction video

The ML Process

Data Collection

ML begins with collecting relevant data. This data serves as the foundation for training and evaluating models.

Feature Extraction:

Features (attributes) are extracted from the data. For instance, in image classification, features might include pixel values or color histograms.

Model Selection:

Choosing an appropriate ML model is crucial. Common models include decision trees, neural networks, and support vector machines..

Training:

The model is trained using labeled data. It learns to map input features to output predictions.

Evaluation:

The models performance is assessed using validation data. Metrics like accuracy, precision, and recall help evaluate its effectiveness..

Fine-Tuning:

Based on evaluation results, the model may be fine-tuned to improve performance..

Deployment:

Once satisfactory, the model is deployed for real-world use.

Real-World Applications

Healthcare

Predicting disease outcomes, diagnosing medical conditions, and drug discovery

Finance

Fraud detection, credit scoring, and stock market predictions.

Natural Language Processing (NLP)

Sentiment analysis, chatbots, and language translation.

Computer Vision

Image recognition, object detection, and self-driving cars.

Machine Learning Introducion

Machine Learning with Python and Scikit-Learn (18 hours course)

Learn PyTorch for Deep Learning (26 hours course)

All code on GitHub

GitHub Repository

its divide in short videos

Mathematics for Machine Learning (9 hours course)

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science