Still confused about what is machine learning? This blog unveils its definition, tools, examples, careers & 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 & that means understanding 'what is machine learning'.
What Is Machine Learning?
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 & 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.
Machine Learning vs Artificial Intelligence vs Deep Learning
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
Artificial intelligence is a broad concept that revolves around machines performing tasks requiring human-like intelligence.
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
Diverse as per the requirement.
Thousands of data points.
Million of data points/ big data.
Management
Human oversight is imperative in ensuring proper functioning.
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
Wide usage like decision-making, predictions, recommendation, etc.
Mostly used for numerical values such as scores or classifications.
Wide usage like free-form elements (generative AI) and numerical values.
Real World Examples of Machine Learning 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
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.
Spam Filtering & Email Automation
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.
Social Media Optimization
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.
Product Recommendations
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.
Financial Accuracy
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.
Self-Driving Technology
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.
Natural Language Processing
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.You May Also Read - Machine Learning Operations Overview
Machine Learning Tools and Technologies
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 Machine Learning
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
Notebooks like Jupyter and Visual Studio Code are used by developers to collaborate.
Responsible AI enables developers to perform in-depth investigations into the models.
Data preparation is iterated at large on Apache Spark clusters. It is interoperable with Azure Databricks.
Managed endpoints help developers in decoupling the production workload's interface from the implementation serving it.
'Designer' is a drag-and-drop ML interface that's used to build ML pipelines.
Amazon SageMaker
Amazon SageMaker is a popular, completely managed service that's crafted to build ML 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
Data wrangler facilitates the users in aggregating and preparing image or tabular data rapidly for ML.
Experiments is a managed service with which users can track and analyze the ML experiments at scale.
Canvas is a no-code interface employed by users for creating ML models. Programming or ML expertise is not needed by users to build their models using Canvas.
Clarify can be used for gaining in-depth insights into the ML models and data. Metrics like toxicity, accuracy, bias and robustness are used as the basis here. It reduced bias in models, leading to enhanced quality.
PyTorch
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
TorchScript is used to create optimizable and serializable models from the PyTorch code. This means it is always production-ready.
Models are exported in the native ONNX format . This gives direct access to ONNX-compatible runtimes, platforms, visualizers, etc.
PyTorch supports distributed training that enables developers to optimize the performance in production and research.
TorchServe is used to simplify the deployment phase of PyTorch models at scale.
Apache Mahout
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
It can be distributed through large datasets and data center clusters on Apache Hadoop.
Proven algorithms are leveraged to sail through common problems faced in different industries.
TensorFlow
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 -
Training is usually sped up with TPU and GPU support .
There are pre-built models for plenty of out-of-the-box use cases.
Distributed computing is supported that enables developers in training models via multiple machines.
TensorBoard is a popular visualization tool that facilitates users in visualizing their model.
Weka
Weka is an accumulation of ML 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 -
It is both cloud-native and datacenter ready . Thus, companies can switch seamlessly between running in the cloud, on-premises or somewhere between locations.
It provides multi-protocol support for NFS, S3, Native NVIDIA GPUDirect Storage, SMB and POSIX.
Wrap-Up
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 professional 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.
FAQs for 'What is Machine Learning'
Q. What is Machine Learning and its advantages?
ML is an AI subset that is transforming how machines imitate intelligent human behavior. ML models are trained to learn, improve and adapt with the changing times and needs of the customers. With its growing usage, it is clear that it aids in enhancing decision-making capabilities, sparks innovation and simplifies tasks.
Q. Who introduced ML?
ML was introduced by Arthur Samuel, in the year 1959. This IBM employee was a true pioneer in revolutionary fields like artificial intelligence and computer gaming. He was the one who invented a program for calculating the winning odds of checkers for both sides.
Q. Is ML a good career?
ML is certainly a good career. Indeed added it on the list of the top 19 jobs in the tech world. This is a great number considering how little time it has been since this technology came into the picture.Course Schedule
