Artificial Intelligence (AI) refers to the general ability of computers to mirror human like thoughts and perform activities in the practical world.
Artificial Intelligence is the field of shaping computers and robots that are capable of behaving in ways that both mirror and exceed human abilities. AI-enabled programs can investigate data to provide information or automatically trigger actions without manual interventions.
Today, artificial intelligence is at the heart of voice assistants such as Siri on Apple devices and many other smart devices are operated using AI. Companies are comprising techniques such as natural language processing and computer vision — the ability for computers to use human language and construe images to mechanize tasks, accelerate decision making, and qualify for conversation via chatbots.
Computer programmers and software developers enable computers to analyse data and providing resolutions to problems; fundamentally, they create AI systems — by applying tools, namely, machine learning, deep learning, computer vision, natural language processing neural networks etc. Major sectors like Corporates, Banking, Stock Markets, E-commerce, etc. make use of AI and Machine Learning for efficient and accurate data analysis, protecting customer data.

Machine Learning (ML) or Predictive Analytics examines the expertise and set of rules that allows the system to analyse patterns, draw conclusions, and advance themselves through involvement, events and statistics.
Machine learning is a fragment of AI that spontaneously permits a machine or system to learn and enhance from learnings and past events. Instead of specific programming, machine learning uses algorithms to analyse large statistics, learn from the understandings, and then make informed decisions.
Being exposed to more data assists machine learning algorithms to improve execution over time. The best output is expected from Machine Learning on the basis of what the learnings are from running statistics and data. Machine Learning is always self-learning. The more information used, better are the results expected.
Few implementations of machine learning use data and neural systems in a way that represents the working of a living brain.

AI vs ML:
Usually, people tend to use artificial intelligence (AI) and machine learning (ML) interchangeably, specifically, when discussing huge data, predictive analytics, and other arithmetical transformation topics. This confusion is bound as artificial intelligence and machine learning are relatively used. Nonetheless, these trending technologies differ in several ways, including scale, tools, applications etc.

• AI is the wider concept of permits a machine or system to sense, reason, act, or adapt like a human
• ML is an application of AI that allows machines to obtain knowledge from data and learn from it independently

Artificial intelligence is the predominant term that covers a wide variety of definite approaches and algorithms. Machine learning fits under that umbrella, just as the other major sub-areas, such as deep learning, robotics, expert systems, and natural language administering and processing.

Advantages of using both – AI and ML:
AI and ML bring powerful benefits to organizations of all appearances and sizes, with new possibilities incessantly evolving. As expected, the number of data possibly only grows with complexity; automated and intelligent systems are fundamental in helping companies systematise tasks, unlock value, and generate accurate insights to achieve better outcomes.

• Analysis of huge data ranges
• Helps in quick decision making
• Intact accuracy and efficiency
• Helps in predictive analytics with fitting insights

Reference:
https://cloud.google.com/learn/artificial-intelligence-vs-machine-learning#section-6
https://en.wikipedia.org/wiki/Machine_learning