Often, you will hear people talk about artificial intelligence (AI) and machine learning (ML) interchangeably. Although that is the case, these trending technologies differ in several ways.
Artificial intelligence is an umbrella term. It refers to a set of technologies used to build computers and machines that behave like humans. AI mimics human intelligence, and it enables machines to reason, learn, and solve complex problems.
But, machine learning is a subset of artificial intelligence. It consists of technologies that allow systems to identify patterns and make decisions. The ultimate goal of ML is to make systems that can improve their performance through experience and data. Data is a crucial component in machine learning.
ML Facts
- ML is an application of artificial intelligence that enables machines to learn without explicitly being programmed
- Machine learning relies on data gathering
- The functions of machine learning can be descriptive, predictive, or prescriptive
- ML uses self-learning algorithms to generate predictive models
The three subcategories of machine learning are:
- Supervised ML – trained with labeled data sets
- Unsupervised ML – a program searches for patterns in unlabeled data
- Reinforcement ML – a training method based on rewarding desired behaviors and/or punishing undesired behaviors
Top 14 Machine Learning Libraries
Traditionally, people used to perform ML tasks manually. They could write codes, design algorithms, and/or use mathematical and statistical formulae to do operations. Gone are those days!
Today, we have powerful technologies like Python that simplify everything. The following are the top 10 Python libraries used in machine learning:
- TensorFlow
- PyTorch
- Scipy
- Keras
- Matplotlib
- Pandas
- Theano
- Numpy
- Scikit-learn
Other languages that support machine learning are C and C++. C offers FANN while C++ has several ML libraries too like:
- OpenNN
- Mlpack
- Shogun
- Armadillo
Each library has a purpose. For instance, Numpy is best known for scientific computation. PyTorch and TensorFlow are recommended for Deep Learning.
Applications of AI and Machine Learning
- Machine learning can identify fraud and prevent cybersecurity attacks, especially in the banking sector.
- Based on AI and ML, biometrics and computer vision can authenticate user identities for document processing.
- These technologies can detect equipment errors before a malfunction occurs.
- The telecommunication industry uses these technologies to automate business operations.
- Manufacturers use AI and ML for production monitoring, predictive maintenance, and operational efficiency.
Last, but not least, it is worth mentioning the Internet of Things (IoT). IoT refers to connecting devices that capture input from multiple sources. An AI-integrated IoT device can reveal patterns by analyzing data. That is why IoT plays a significant role in this era of artificial intelligence and machine learning. If we dive deeper into Business Intelligence, we will see how these technologies can help us adjust operations to promote efficiency.
Do you want to learn more about AI and machine learning?