In our day-to-day activities, we need the intervention of machines and humans. Machines are reducing human efforts to some extent, but still, there is a need for great human effort. Here comes the need for machine learning. Today it is making its path to almost every field in our day-to-day activities. Machines are trained to do tasks with reduced human intervention.
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Today businesses that are implementing it in their day-to-day tasks are increasing day by day. Ecommerce sites use it to personalize customers to provide better experiences and services. The banking and financial industry is using it for fraud detection as well as fraud prevention. The health care field is using image analysis for analyzing images of body parts. It helps diagnose diseases. As per Forbes, the global machine learning market was valued at 1.58 billion US dollars in 2017 and is expected to reach 20.83 billion US dollars in 2024 with a compound annual growth rate(CAGR) of 44.06 percent.
Why do we need ML Tools?
As machine learning usage is increasing daily, some people are eager to learn about the technology. Some people are already working in the field AI. Some professionals are eager to update their knowledge. Some people are trying to switch to this field. So the blog can give them an idea of its tools, their examples, their features, and their limitations. This will be helpful for them.
What are Machine Learning tools?
Its tools are artificial intelligence algorithmic applications that allow the systems to understand and improve without human intervention. Tools are essential for the following reasons:
It is an open-source library that trains and builds its models. Google Brain Team developed it. For training and building models, it provides high-level Keras API. Features of TensorFlow are as follows:
It also allows running the existing models with TensorFlow.js.
It provides support for distributed computing.
It enables rapid iteration and intuitive debugging.
It runs on GPUs and CPUs.
It is an open-source framework based on the Torch library. Facebook's AI research lab develops it. It has a python and c++ interface. Here are the features of PyTorch:
It enables the creation of neural networks using the Autograde module
It is more apt for deep learning research.
It can provide a dynamic computational graph.
It includes tutorial course tools and libraries.
It can also be helpful on cloud platforms.
Google Cloud ML Engine
While dealing with massive data, local systems do not perform well. In that case, we can use Google Cloud Engine. It is a collaborative platform where its engineers and data scientists can collaborate on their work. Its features are:
It helps in model training, building, and predictive modeling.
In this platform, we use training and prediction combinedly or independently.
It helps to train complex models.
It can be helpful at an enterprise level.
It is a cloud-based and robust application. It integrates data from multiple sources. Its features are:
It permits the import and export of the model to and from AML, respectively.
It provides the core concept.
It helps users to retrieve predictions with the help of batch API.
It is an open-source project of the Apache Software Foundation. It is used for developing its applications mainly based on Linear Algebra. It is a distributed Linear Algebra framework. It enables developers to implement their algorithms. It provides java and scala libraries for mathematical operations. Its features are:
It is the best framework for implementing scalable algorithms.
It consists of matrix and vector algorithms.
It can provide support for multiple distributed backends.
It is a free and open-source library.It was created by Gunnar Raetsch and Soeren Sonnenburg in the year 1999.Shogun's primary aim is kernel-based algorithms such as k-means clustering and supports vector machines. It helps in the complete implementation of Hidden Markov Models. Its features are:
It enables users to work in different programming languages such as Lua, Python, Java, C#, Octave, MATLAB, and R.
It also offers the usage of combined kernels.
It can process massive data sets.
Apache Spark MLlib
It is a scalable library. It can run on Apache Mesos, Hadoop, Kubernetes, standalone, and the cloud. It can access different data sources. It is an open-source cluster computing framework. Its features are:
It has high-quality algorithms.
It is easy to use as it provides Python, Java, Scala, SQL, and R interfaces.
It is a cloud platform that allows for model training, building, and deployment. It has the following features:
Shared notebooks, compute resources, data, and environments
Tracking and audibility
Scikit-Learn is a powerful and robust library used for it in Python. It gives a selection of efficient tools for ML and statistical modeling. This library is primarily written in Python and is built upon Numpy,scipy, and matplotlib.
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What are the challenges?
The most common challenges are listed below:
Lack of Connectivity
One of the main problems in adopting the its model and tools is the lack of connectivity. Consider a school in a remote village with a lack of connectivity. So it will be difficult for the school management to adopt these tools.
While using its tools takes some time to download and install. We can consider it normal. But when preparing a model with some specific algorithms consumes a lot of time.
Slow-down of a local system
People who are studying it don't use cloud platforms. They use their local system. For running models, it requires considerable memory. Running a large amount of data in local systems makes the system slower.
While running a model in a jupyter notebook, some versions of specific tools do not work with the given environment. So there comes the need for upgrading the version or downgrading it. This will consume time and lead to a lack of continuity in building or running the model.
Future Scope and its Latest Trends
The future trends are described below:
Sensors and IoT devices help in optimizing manufacturing and supply chain management.
The renewable energy industry is using Artificial Intelligence to mitigate the unpredictability of sources.
Retailers use ML technologies to monitor body temperature and mask-wearing with the help of thermal imaging and computer vision.
Banks and financial enterprises are using its algorithms to prevent fraudulent activities.
Not only financial institutions but every business is also now changing to ML for spam detection in emails, fraudulent detection, and fraud monitoring.
E-commerce and media platforms are using machine learning to provide highly personalized experiences to customers.
The face swap filters, such as snap chat filters, use computer vision to detect and exchange facial features.
Robots are performing complicated surgeries
ML programs read patient history, records, and reports and instruct treatment plans. e.g., IBM Watson Oncology
Wearables for prevention of diseases and health care monitoring.
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What are the best Use Cases?
The best use cases of ML are listed below:
In the banking and financial sector, it is used for fraud detection for secure transactions. AI and deep learning are used to detect fraudulent behaviors. Organizations across the globe are using sentiment analysis for stock market predictions. Budget management applications use it to help customers track their expenses, analyze spending patterns, and give advice on money management.
ML is used in content creation and curation. These processes are a little complex and necessary in this digital era. Nowadays, its tools are used for this process to make them accessible. It is used to optimize the experience of customers. Reinforced learning is used in the marketing field for customer acquisition and retention.
Convolutional Neural Networks(CNN) are used in healthcare to recognize and classify images. It effectively diagnoses skin cancer with an accuracy of 95 percent. It is beneficial in pandemic management. The most recent example is the covid-19 mortality predictor. This can help patients from death.
It is used in email monitoring. Email is one of the professional communication mediums. So there is a chance for cyber security attacks. Machine learning techniques help detect cyber threats with the help of real-time email monitoring. Bots contribute approximately 25 percent of the web traffic. Some of these bots are malicious. Sometimes it takes control of the entire application.
Machine learning has a wide range of capabilities. It's a data-driven approach. So it is more accurate than the traditional approaches. Now we are using its tools with sound capabilities. In the coming years, the technological world will see the emergence of advanced tools. If the capabilities of tools increase, we can do more adventurous things with it. Now things are going great. In the coming years, it will be greater.