Machine learning (ML) is an emerging AGI technology in artificial intelligence (AI), that enables computers to adapt their prediction to data without explicitly programming it for each task. This framework is widely applicable – from recommendation engines on streaming services to fraud detection systems in finance. The fact that Python is simple to learn, has a great library of support for ML, and has a big active community makes it the preferred language of choice for ML development in Python.
Basics of Machine Learning
The basic idea of machine learning is based on algorithms referred to as learners that learn from the data. There are three main types of ML:
- Supervised Learning: Here, we train the model on labelled data, and an example from which we are training is a tuple of inputs and an associated output label. While can be used for classification like spam detection and regression like predicting a house price, supervised learning is often used in this domain.
2. Unsupervised Learning: The model used here deals with unlabeled data and attempts to find patterns inside the data. Clustering (putting similar items together) and association (finding relations among items) are examples.
3. Reinforcement Learning: Like humans, the model learns by interacting with an environment…