Still confused about what is machine learning? This blog unveils its definition, tools, examples, careers and much more. Understand how ML is different from artificial intelligence & deep learning by reading ahead!
Businesses must understand the technologies that drive innovation to always stay ahead of the curve. With all the digital transformations that are happening all around us, staying updated is more of a necessity than a choice. Machine learning (ML) is the one technology that must be understood and rightly implemented by organizations to ensure success.
This blog is a dive into the top key aspects of this technology. Beginning with its definition, tools, examples and how it compares to AI and deep learning, everything's covered. The most important question needs to be answered first and that means understanding 'what is machine learning'.
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If you come to think about it, machine learning is more widely present than we usually care to estimate. Not sure what we're talking about? How do computers recognize faces in pictures, beat humans at games and translate languages? Machine learning is what goes on behind-the-scenes! But what is machine learning? Machine learning is all about teaching computers to engage in self-learning. This is done by feeding a model gigantic amounts of data during the training phase. This prepares it to learn from patterns in new and old data without being programmed explicitly every single time. It has stemmed from artificial intelligence (AI) but thus, these two are not interchangeable terms.
In 2022, the market size for machine learning was $19.20 billion globally. By 2030, this number is expected to reach $225.91 billion, showcasing a staggering CAGR of 36.2%. These numbers are derived from a report published by Fortune Business Insights.
There are many similar looking technologies out there. This can make it a bit difficult to understand how exactly they are different from one another. Since most people confuse between these, here's a look into machine learning vs artificial intelligence vs deep learning.
BASIS | ARTIFICIAL INTELLIGENCE (AI) | MACHINE LEARNING (ML) | DEEP LEARNING (DL) |
Concept | Machine learning revolves around developing algorithms that learn from patterns in data. | Machine learning revolves around developing algorithms that learn from patterns in data. | Deep learning employs complex neural networks that work like the human brain. Humongous amounts of data are studied to learn intricate patterns. |
Optimal Data Volumes | Thousands of data points. | Thousands of data points. | Million of data points/ big data. |
Management | Data analysts direct the algorithms with inputs and outputs (not always). It consequently learns to work without explicit programming. | Data analysts direct the algorithms with inputs and outputs (not always). It consequently learns to work without explicit programming. | Data analysis is the basis of its largely self-directed working. |
Usage | Mostly used for numerical values such as scores or classifications. | Mostly used for numerical values such as scores or classifications. | Wide usage like free-form elements (generative AI) and numerical values. |
The real world examples of machine learning are increasing with every passing day. This technology is spearheading into the future with exceptional possibilities and various use cases.
Facial recognition is a big use case and benefit of ML. It has positively transformed various aspects of our everyday tasks and activities. This example, paired with deep learning, is leaving an impact on different industries and sectors. It aids in combating key social issues like detecting thiefs, trafficking, and smuggling. In healthcare, it tracks a patient's history of drug abuse/ medication and detects genetic diseases.
Successful implementation of machine learning leads to email automation and spam filtering. This area is especially in focus for organizations that rely mostly on outbound leads. Spam filtering means getting a better grasp of the patterns that make the email content undesirable.
All big platforms like Facebook, Twitter and Instagram utilize AI, ML and big data to enhance the use experience and offer better functionality. ML has played a big role here in tackling cyberbullying and inappropriate content. Both these risk the platform in losing its integrity and getting a tarnished image. When data is processed via deep neural networks (DNNs), these platforms get a better understanding of their user preferences. This leads to target advertising and relatable content suggestions.
Targeted marketing in retail uses ML to learn about user preferences. Their demographic similarities, buying habits and purchasing history are used to train the models. Depending on how good quality and accurate the data is, the predictions can either be bang on or way off. Not buying or clicking through a product also creates a data point for future reference.
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The finance industry is benefitting tremendously with the rise of ML. It's tiring and almost impossible for the human eye to analyze the increasing number of transactions. But with ML, analysis is done swiftly, helping in finding fraudulent transactions. Other uses include assessing credit scores to determine lending decisions and doing risk analysis. AI, natural language processing and ML come together to enhance a customer's banking experience.
ML powers the self-driving technology wherein sensors are used to collect data surrounding the car in real-time. This real-time data is employed to guide the car and navigate its response in varying situations. These could be an animal/ human crossing the road, red light or another vehicle.
Speech signals are supported and manipulated into text and commands by language models. This is pretty similar to how ML recognizes images. A software app that's coded with AI has the potential to convert live and recorded speech into text files.
Having reliable machine learning tools and technologies by one's side is integral to ensure success. It's a long road to gain prowess in all the top ones but now's a great time to get started.
Microsoft Azure ML is a 360-degree managed cloud service. It helps developers and data scientists in deploying, building and managing the entire ML project lifecycle. All this happens faster and with great precision. The time to value is accelerated with MLOps, integrated tools and open-source interoperability.
Key feature include-
Amazon SageMaker is a popular, completely managed service that's crafted to build Machine Learning models and generate predictions. This platform is used by developers for building, deploying and training the ML models at large. It's done in a single IDE (integrated development environment) by using different tools. Governance requirements are supported via transparency and simplified access control over the ML project. It's particularly beneficial because of its wide array of tools and multi-framework support.
It's key features are-
PyTorch refers to an optimized and open-source tensor library. It is most often used to act as a support pillar in developing DL models with GPUs and CPUs. It boasts a vibrant community, rendering a great learning experience. Its dynamic computation graph enables the models in creating and modifying on the fly.
Top features are-
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Apache Mahout is a distributed, open-source linear algebra framework. Additionally, it's a mathematically expressive Scala DSL (domain-specific language). It's implemented on Apache Hadoop and crafted for mathematicians, data scientists and statisticians primarily. The biggest perk of Apache Mahout is its scalability. Different professionals employ this framework for building efficient and scalable implementations rapidly.
Key features include-
TensorFlow is an open-source and 360-degree ML platform by Google. It is primarily associated with the inference and training of deep neural networks. Additionally, it provides plenty of libraries and tools like TensorFlow Serving, enabling users to train, deploy and build ML models.
Key features of TensorFlow are-
Weka is an accumulation of Machine Learning algorithms for various data mining tasks. It comprises plenty of tools for data visualization, preparation, clustering, association rules mining, regression and classification. This one platform aids companies in processing, managing and storing their data both on premises and in the cloud.
Key features of Weka include-
Machine learning is an amazing technology that equips systems with the prowess to learn from old and new data. This eliminates the need for explicit programming every single time. It's a huge technology with endless possibilities and use cases. This blog covers the definition of 'what is machine learning' along with its real-world examples, tools and technologies. Becoming a machine learning engineer is a great career option. To launch a career here, good training holds utmost importance.
Since there is no one specific certification in this field, any good, widely recognized credential can be chosen. In fact, certification is not even a prerequisite to get a job. Expertise, knowledge and skills are the biggest counting factors.
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