What is AI?

  • The ability of a computer program or a machine to think and learn and act in a way that it exhibits intelligence
  • Incorporated human intelligence into machines
  • Amazon: “the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.”

What is Machine Learning

Machine learning is an algorithm, or model that learns patterns and data, and then predicts similar patterns in new data

Machine learning is an application of AI (subset of AI) that provides systems the ability to learn from data without being explicitly programmed ability of algorithms to learn from data by training

Machine learning allows it to take existing data, analyze it to identify patterns, and use the results to make better predictions about new data.

AI Machine Learning and Deep Learning

Types of Machine Learning

Supervised Learning

Supervised learning is a machine learning technique where: algorithm learns from labeled training data. It means some data is already tagged with the correct answer. It can be compared to learning which takes place in the presence of a supervisor or a teacher. If you’re learning a task under supervision, someone is present judging whether you’re getting the right answer. Similarly, in supervised learning, that means having a full set of labeled data while training an algorithm labeled means that each example in the training dataset is tagged with the answer the algorithm should come up with on its own. So, a labeled dataset of flower images would tell the model which photos were of roses, daisies and daffodils. When shown a new image, the model compares it to the training examples to predict the correct label

Unsupervised Learning

Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. algorithm learns from unlabeled training data. Unsupervised learning is a machine learning technique that finds and analyzes hidden patterns in “raw” or unlabeled data. By ignoring labels altogether, a model using unsupervised learning can infer subtle, complex relationships between unsorted data that semi-supervised learning (where some data is labeled as a reference) would miss. And do so without the time and costs needed for supervised learning (where all data is labeled). More:

Reinforcement learning

Reinforcement learning, in the context of artificial intelligence, is a type of dynamic programming that trains algorithms using a system of reward and punishment. A reinforcement learning algorithm, or agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly. The agent learns without intervention from a human by maximizing its reward and minimizing its penalty Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward

What Is Semi-Supervised Learning?

Think of it as a happy medium.

Semi-supervised learning is, for the most part, just what it sounds like: a training dataset with both labeled and unlabeled data. This method is particularly useful when extracting relevant features from the data is difficult, and labeling examples is a time-intensive task for experts.

Data science and AI and machine learning

  • Artificial intelligence => AI means algorithms that learn from data
  • ML => the ability of algorithms to learn from data and to learn in such a way that they can improve their function in the future
  • Data science => collection of skills and techniques for dealing with challenging data
  • Big Data => 3 V’s (Volume, Variety, Velocity)