Machine learning and artificial intelligence

 

1. INTRODUCTION
1.1 Journal Overview

This Journal is mainly focused about Machine learning and artificial intelligence. This journal is a reflective write-up on what the webinar was about, what was learnt from it and how the knowledge gained from it can be applied to my future career.

                               

1.2 List of Acronyms and Abbreviations

AI - Artificial intelligence 

ML – Machine Learning

 

2. Machine learning and artificial intelligence
2.1 Summary

Intelligence is the ability to think, to learn from experience, to solve problems, and to adapt to new situations. 

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

 

2.2 Learning Outcome

AI is transforming the world around us, creating an avenue to innovation across all sectors of the global economy. Today, AI can interact with humans through natural language; identify bank fraud and protect computer networks; drive cars around city streets; and play complex games like chess and Go. Machine-learning is offering solutions to many complex problems around us where analytical solutions may be too expensive or practically impossible.

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

 

2.3 What is Intelligence?

Intelligence has been defined in many ways: the capacity for logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving.

 

2.4 Artificial Intelligence

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

 

2.5 Machine Learning

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

   




2.6 Deep Learning

Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.

  

2.7 AI vs Machine Learning vs Deep Learning

Artificial Intelligence is the broader umbrella under which Machine Learning and Deep Learning come. And you also see in the diagram that even deep learning is a subset of Machine Learning. So, all three of them AI, machine learning and deep learning are just the subsets of each other. 

 


2.8 Statical ML Models

A Statistical Model is the use of statistics to build a representation of the data and then conduct analysis to infer any relationships between variables or discover insights. Machine Learning is the use of mathematical and or statistical models to obtain a general understanding of the data to make predictions.

Statistical modelling is a method of mathematically approximating the world. Statistical models contain variables that can be used to explain relationships between other variables. We use hypothesis testing, confidence intervals etc to make inferences and validate our hypothesis.

The classic example is Regression in which we take a single or number of variables to find the effect of each explanatory variable to the independent variable.

A statistical model will have sampling, probability spaces, assumptions, and diagnostics etc, to make inferences.

We use statistical models to find insights given a particular set of data. We can conduct modelling on a relatively small set of data just to try and understand the underlying nature of the data.

Inherently all statistical models are wrong or not perfect. They are used to approximate reality. Sometimes the underlying assumptions of the model are far too strict and not representative of reality.

Statistics is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it.


 




2.9 Neural Networks

Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.

  










2.10 Statical ML Models vs Neural Networks

While a Machine Learning model makes decisions according to what it has learned from the data, a Neural Network arranges algorithms in a fashion that it can make accurate decisions by

itself. Thus, although Machine Learning models can learn from data, in the initial stages, they may require some human intervention.

Machine Learning uses advanced algorithms that parse data, learns from it, and use those learnings to discover meaningful patterns of interest. Whereas a Neural Network consists of an assortment of algorithms used in Machine Learning for data modelling using graphs of neurons.

While a Machine Learning model makes decisions according to what it has learned from the data, a Neural Network arranges algorithms in a fashion that it can make accurate decisions by itself. Thus, although Machine Learning models can learn from data, in the initial stages, they may require some human intervention.

Neural networks do not require human intervention as the nested layers within pass the data through hierarchies of various concepts, which eventually makes them capable of learning through their own errors.

An ML model works in a simple fashion – it is fed with data and learns from it. With time, the ML model becomes more mature and trained as it continually learns from the data. On the contrary, the structure of a Neural Network is quite complicated. In it, the data passes through several layers of interconnected nodes, wherein each node classifies the characteristics and information of the previous layer before passing the results on to other nodes in subsequent layers.

Since Machine Learning models are adaptive, they are continually evolving by learning through new sample data and experiences. Thus, the models can identify the patterns in the data. Here, data is the only input layer. However, even in a simple Neural Network model, there are multiple layers.

These are some of the major differences between Machine Learning and Neural Networks. Neural Networks are essentially a part of Deep Learning, which in turn is a subset of Machine Learning. So, Neural Networks are nothing but a highly advanced application of Machine Learning that is now finding applications in many fields of interest. 

 

2.11 Machine Learning Everywhere

Most of the industries dealing with huge amounts of data have now recognized the value of machine learning. By gleaning hidden insights from this data, businesses can work more efficiently and can also gain a competitive edge. Besides, affordable and easy computational processing and cost-effective data storage options have made it feasible to develop models that quickly and accurately analyse huge chunks of complex data. Apart from enabling enterprises to identify trends and patters from diverse data sets, ML also enables businesses to automate analysis, which was traditionally done by humans. Using ML organizations can deliver personalized services and differentiated products that precisely cater to varying needs of the customers. Additionally, ML also helps companies to identify opportunities that can be profitable in the long run.

3. Conclusion

 The influence and impact of machine learning can be seen in everything from our morning coffee orders to the online banking apps we use.

The technology is infusing a deeper intelligence and understanding into the applications that touch our lives, to dramatically improve our experiences. In addition, it’s helping to spawn entirely new business innovations and models, such as autonomous vehicles and virtual personal assistants.

In fact, machine learning is so prevalent and pervasive that it’s difficult to imagine an enterprise being able to survive without embracing it in the next five years. Especially considering predictions of continued global data growth. This is why it is not only important, but critical to understand the current state of machine learning and what the future holds.

Skills required for Machine Learning include programming, probability and statistics, Big Data and Hadoop, knowledge of ML frameworks, data structures, and algorithms. Neural networks demand skills like data modelling, Mathematics, Linear Algebra and Graph Theory, programming, and probability and statistics.

Machine Learning is applied in areas like healthcare, retail, e-commerce (recommendation engines), BFSI, self-driving cars, online video streaming, IoT, and transportation and logistics, to name a few. Neural Networks, on the other hand, are used to solve numerous business challenges, including sales forecasting, data validation, customer research, risk management, speech recognition, and character recognition, among other things.

 

 

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