AI & Data Science

AI & Data Science

Artificial Intelligence is a part of Data Science. Data Science is not just about Machine Learning, it consists of mining the data, cleaning up, making sense out of it, visualizing, etc. After all this, if there are any hopes for the data to be modeled, then you go pick up the AI.

Data science is the practice of transforming data into knowledge, and R is one of the most popular programming language used by data scientists. In a data-driven economy, this combination of skills is in extremely high demand, commanding significant increases in salary, as it is revolutionizing the world.

Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters. This encompasses many techniques such as regression, naive Bayes or supervised clustering.

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Difference Between Machine Learning, Data Science and Artificial Intelligence

Even though the terms data science, machine learning, and artificial intelligence (AI) fall in the same domain and are connected to each other, they have their specific applications and meaning. There may be overlaps in these domains every now and then, but essentially, each of these three terms has unique uses of their own. 

We will start with the term Data Science, as it assumes the top-most position in the hierarchy of data-related technologies.

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Scope of Data Science

  • Predictive causal analytics: Data scientists use this model to derive business forecasts. The predictive model showcases the outcomes of various business actions in measurable terms. This can be an effective model for businesses trying to understand the future of any new business move.
  • Prescriptive Analysis: This kind of analysis helps businesses set their goals by prescribing the actions which are most likely to succeed. Prescriptive analysis uses the inferences from the predictive model and helps businesses by suggesting the best ways to achieve those goals.
  • Machine Learning for Predictive Reporting: Data scientists use machine learning algorithms to study transactional data to make valuable predictions. Also known as supervised learning, this model can be implemented to suggest the most effective courses of action for any company
  • Machine Learning for Pattern Discovery: Pattern discovery is important for businesses to set parameters in various data reports and the way to do that is through machine learning. This is basically unsupervised learning where there are no pre-decided parameters. The most popular algorithm used for pattern discovery is Clustering.

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Scope of AI

  • Automation is easy with AI: AI allows you to automate repetitive, high volume tasks by setting up reliable systems that run frequent applications.
  • Intelligent Products: AI can turn conventional products into smart commodities. AI applications when paired with conversational platforms, bots and other smart machines can result in improved technologies.
  • Progressive Learning: AI algorithms can train machines to perform any desired functions. The algorithms work as predictors and classifiers.
  • Analysing Data: Since machines learn from the data we feed them, analysing and identifying the right set of data becomes very important. Neural networking makes it easier to train machines.

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Scope of Machine Learning

  • Supervised machine learning: This model uses historical data to understand behaviour and formulate future forecasts. This kind of learning algorithms analyses any given training data set to draw inferences which can be applied to output values. Supervised learning parameters are crucial in mapping the input-output pair.
  • Unsupervised machine learning: This type of ML algorithm does not use any classified or labelled parameters. It focuses on discovering hidden structures from unlabelled data to help systems infer a function properly. Algorithms with unsupervised learning can use both generative learning models and a retrieval-based approach.
  • Semi-supervised machine learning: This model combines elements of supervised and unsupervised learning yet isn’t either of them. It works by using both labelled and unlabelled data to improve learning accuracy. Semi-supervised learning can be a cost-effective solution when labelling data turns out to be expensive.
  • Reinforcement machine learning: This kind of learning doesn’t use any answer key to guide the execution of any function. The lack of training data results in learning from experience. The process of trial and error finally leads to long-term rewards.

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