Artificial Intelligence, Machine learning, and Deep learning are frequently used terms in today’s scenario. Some also relate Statistics with AI, Machine learning and Deep learning.everyone wants to know how they are differ and related, here I am trying to answer this query, Before going to start with Statistics I am explaining about Artificial Intelligence, Machine learning, and Deep learning,

The easiest way to think of their relationship is to visualize them as concentric circles with AI  the idea that came first the largest, then machine learning which blossomed later, and finally deep learning which is driving today’s AI explosion fitting inside both.

Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Machine learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.

Machine learning came directly from minds of the early AI crowd, and the algorithmic approaches over the years included decision tree learning, inductive logic programming. Clustering, reinforcement learning, and Bayesian networks among others.

Deep Learning is another algorithmic approach from the early machine-learning crowd, Artificial Neural Networks, came and mostly went over the decades. Neural Networks are inspired by our understanding of the biology of our brains – all those interconnections between the neurons.

Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon. AI is the present and the future. With Deep Learning’s help, AI may even get to that science fiction state we’ve so long imagined.

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Now it is clear that AI and Deep learning are related to each other, So here we are discussing difference between Machine Learning and Statistics Modelling. Machine Learning has adopted many of Statistics’ methods, but was never intended to replace statistics, or even to have a statistical basis originally. Nevertheless, Statisticians and ML practitioners have often ended up working together, or working on similar tasks, and wondering what each was about. The question, “What’s the difference between Machine Learning and Statistics?” has been asked now for decades.

Let’s start with simple definitions

  • Statistical modelling
    • is the formalization of relationships between variables in the form of mathematical equations.Statisticians are heavily focused on the use of a special type of metric called a statistic. These statistics provide a form of data reduction where raw data is converted into a smaller number of statistics. Two common examples of such statistics are the mean and standard deviation.
  • Machine learning
    • is an algorithm that can learn from data without relying on rules-based programming.Machine Learning has had many twists and turns in its history. Originally it was part of AI and was very aligned with it, concerned with all the ways in which human intelligent behaviour could be learned. In the last few decades, as with much of AI, it has shifted to an engineering/performance approach, in which the goal is to achieve a fairly specific task with high performance. In Machine Learning, the predominant task is predictive modelling: the creation of models for the purpose of predicting labels.
Here are some differences

#Scope: Machine learning uses statistical models, but it also uses other models such as dynamic programming, reinforcement learning, techniques that came from Artificial Intelligence or optimization.

#Point of View: Statistics is usually concerned with the properties of the estimators (un biasedss, asymptotic behavior) and machine learning is mainly concerned with the solution of real world problems.

#Research field: While Statistics can be seen as a subfield of Applied Mathematics, Machine Learning can be seen as a subfield of computer science.

#Code development and application: While people who work with statistics usually has a preference for R (or SAS, STATA, EVIEWS), people who work with machine learning usually chooses Python (or another structured programming language)

Here is an interesting Venn diagram on the coverage of machine learning and statistical modelling in the universe of data science (Reference: SAS institute)

Differences exist between machine learning and statistical modelling, but the ultimate goal is the same – learn from data. Read on to determine which approach works better for you.


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