What will we learn?

Difference between:

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Deep Learning (DL)
• Data Science (DS)

Understanding the concept in terms of a career in these fields.

Artificial Intelligence

• Simulation of human capability over the machines. Using AI we can create a system to simulate human intelligence.

• Replacing a human which can take the decision exactly like that of a human decision even if it has not faced the situation in the past with efficiency.

• Three types:

• Weak AI: it has comparatively low intelligence than a human does.

• General AI: its intelligence is equivalent to that of a human.

• Strong AI: Its intelligence is comparatively better than that of a human.

The research area is towards the Strong AI concept where hypothetical decisions can be taken.

Applications like Siri, Ok Google, Alexa, and Cortana are a few examples of AI. Another great example would be AlphaGo, a computer program that defeated a champion.

Machine Learning (ML)

• It is a way of analyzing, understanding, and identifying patterns in the data.
• It allows machines to learn from the data that already exists.

• Helps take future decisions better,

• Three types of ML:

  • Supervised Learning: it is the machine learning task of learning a function that maps a target (an input) to a result (an output) based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.

  • Unsupervised Learning: Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of target (input ) data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

  - Semi-supervised Learning: Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data (non-empty data) with a large amount of unlabeled data(missing information) during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data).

Artificial Intelligence vs Machine Learning

• AI mimics human behavior, ML takes the data and gives the output upon training.

• AI is a larger level concept, where ML is a subset of AI.

• The primary focus of AI is to design a smart system that can make decisions equivalent to humans whereas the primary focus of ML is learning from the data and generate correct corresponding output.

• AI can perform new tasks whereas ML can only learn from the historic data on which the algorithm is trained.

Deep Learning (DL)

• Software solution that mimics the behavior of the neurons.
• DL concept depicts the deep network of neurons.
• Takes information, passes through a network of neurons, and gives the solution.

Machine Learning vs Deep Learning

• Deep Learning is a subset of Machine Learning and a super subset of Artificial Intelligence.
• Machine Learning contains algorithms like tree-based algorithms whereas DL contains neuron related algorithms.
• DL is a special class of ML algorithms.

Data Science (DS)

• It is a field to collect the data, visualize it, create a model over it, and communicate the proper result.
• It is a blend of tools to retrieve hidden patterns from the data.
• Shares the work with AI, ML, DL, and focuses on other aspects of data as well.
• It covers data integration, visualization, data dashboard, engineering deploying, and automated decision. It is an end to end solution to solve any real-world problem focusing on practical application.

Data Science (DS) vs (AI+ML+DL)

• DS is connected with all the fields to get real-world predictions and getting decisions for business problems.