Machine Learning, a sub-field of Artificial Intelligence, is playing a key role in a wide range of industry critical applications such as data mining, natural language processing, image recognition, and many other predictive systems.
The goal of Machine Learning is to understand the structure of different data sets and build models that can be understood and utilized by industry systems and people.
It provides automated study and extraction of insights from data by the means of different ML approaches, algorithms, and tools.
In this competitive age, how you use the data to better understand your systems and their behavior will determine the level of success in the market. Machine Learning goes beyond traditional Business Intelligence and accelerates data-driven insights and knowledge acquisition.
Although, it has been around for decades; due to the pervasiveness of data that is being generated and infinite scalability of computing power, it has taken the center stage now.
In this comprehensive guide, we will discuss methods, ML frameworks, predictive models and a wide range of machine learning applications for major industries.
Common approaches to Machine Learning
If you want to predict the traffic of a busy street for a smart city initiative, you can run it through ML algorithms and feed the past traffic data to accurately predict future traffic patterns. Industrial problems are complex in nature which means we must invent new but very specialized algorithms that can solve impractical problems.
Hence, there are different approaches to which ML models can be applied to train a software system.
It takes place when a developer provides the learning agent with a precise measure of its error which can be directly compared with specified outputs. In generic terms, supervised learning is ideal when you have what you want the machine to learn.
The algorithm is mostly trained on a pre-defined set of training examples which enable the program to reach an accurate conclusion when given new data.
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In unsupervised learning, data is unlabelled, so the learning algorithm is left to find commonalities among its input data. As unlabelled data are more abundant than labeled data, machine learning methods that facilitate unsupervised learning are particularly valuable.
The goal of unsupervised learning may be as straightforward as discovering hidden patterns within a dataset, but it may also have a goal of feature learning, which allows the computational machine to automatically discover the representations that are needed to classify raw data.
Unsupervised learning is commonly used for transactional data. You may have a large dataset of customers and their purchases, but as a human, you will likely not be able to make sense of what similar attributes can be drawn from customer profiles and their types of purchases. With this data fed into an unsupervised learning algorithm, it may be determined that women of a certain age range who buy unscented soaps are likely to be pregnant, and therefore a marketing campaign related to pregnancy and baby products can be targeted to this audience to increase their number of purchases.
Without being told a “correct” answer, unsupervised learning methods can look at complex data that is more expansive and seemingly unrelated to organize it in potentially meaningful ways. Unsupervised learning is often used for anomaly detection including for fraudulent credit card purchases, and recommender systems that recommend what products to buy next. In unsupervised learning, untagged photos of dogs can be used as input data for the algorithm to find likenesses and classify dog photos together.
Machine Learning frameworks
In this section, we will talk about some of the best frameworks and libraries available for Machine Learning. Each of these Frameworks is different from each other and takes much time to learn. During the time of making this list, we took care of features other than the basic ones: user base and community & support was one of the most important parameters. Some frameworks are more mathematically oriented, and hence geared more towards statistical than neural networks. Some of them provide a rich set of linear algebra tools; some are mainly focused only on deep learning.
Tensor Flow is developed by Google to write and run high-performance numerical computation algorithm. It is an open source ML library for data-based programming which uses data flow graphs. Tensor Flow offers an extensive amount of functions and classes that we can use to build various training models from scratch.
Earlier, we talked about different machine learning methods, Tensor Flow is capable to handle all kinds of regressions, classifications algorithms and neural networks on both CPUs & GPUs. However, most of the functions are complex so it’s difficult to implement at the early stages.
What makes Tensor Flow the perfect library for enterprises:
- Based on Python API
- Truly portable as it can be deployed on one or more CPUs or GPUs and can be served simultaneously on mobile, computer with a single API.
- It’s flexible enough to run it on Android, Windows, iOS, Linux and even Raspberry Pi.
- It has checkpoints to manage all your experiments
- The community is large to help with any issues.
- Acceptability across the Industries as tons of innovation projects are using TensorFlow.
- It lets you handle the derivatives automatically.
Tensor Flow is being used by the top most companies in the world including:
- And Tons of new-age startups
Spark is an analytics engine based on a cluster-computing framework built for large-scale data processing. The initial development was done at Berkeley’s lab but later was donated to Apache Software Foundation.
With some advanced features, it creates spark label vectors for you thus carrying away much complexity to feed to ML algorithms.
Advantages of Spark ML:
- Simplicity: Simple APIs familiar to data scientists coming from tools like R and Python
- Scalability: Ability to run same ML code on small as well as big machines
- Streamlined end to end
Caffe is an open source framework under a BSD license. CAFFE (Convolutional Architecture for Fast Feature Embedding) is a deep learning tool which is mainly written in CPP.
It supports many different types of architectures for deep learning focusing mainly on image classification and segmentation. It also supports Graphic and CPU based acceleration for neural based engines
CAFFE is mainly used in the academic research projects and to design startups Prototypes. Even Yahoo has integrated caffe with Apache Spark to create CaffeOnSpark, another great deep learning framework.
Advantages of Caffe Framework:
- Caffe is one of the fastest ways to apply deep neural networks to the problem
- Supports out of box GPU training
- Well organized Mat lab and python interface
- Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
- Speed makes Caffe perfect for research experiments and industry deployment.
- Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convent implementations available.
Torch is also a machine learning open source library, a proper scientific computing framework. Its complexity is relatively simple which comes from its scripting language interface from Lua programming language interface. There are just numbers (no int, short or double) in it which are not categorized further like in any other language. So, it eases many operations and functions.
Torch is used by Facebook AI Research Group, IBM, Yandex, and the Idiap Research Institute, it has recently extended its use for Android and iOS.
Advantages of torch framework include:
- Flexible to use
- High level of speed and efficiency
- Availability of tons of pre-trained ML models
Scikit-Learn is a very powerful free to use Python library for ML that is widely used in Building models. It is founded and built on foundations of many other libraries namely SciPy, Numpy, and matplotlib, it is also one of the most efficient tools for statistical modeling techniques.
Advantages of Sci-Kit Learn:
- Availability of many of the main algorithms
- Quite efficient for data mining
- Widely used for complex tasks
Business/Operational Challenges while implementing Machine Learning
To better understand how ML may benefit your organization — and to weigh this against the potential costs and downsides of using it — we need to understand the major strengths and challenges of ML when applied to the business domain.
High performance, efficient, and intelligence
ML can deliver valuable business insights more quickly and efficiently than traditional data analysis techniques because there’s no need to program every possible scenario or require a human to be part of the process — taking people out of the process. ML can process higher volumes of data, it also has the potential to perform much more powerful analytics. ML’s intelligence, provided by its ability to learn autonomously, can be used to uncover latent insights.
Due to higher volumes of data collected by increasingly computing devices and software systems, ML can now be applied to a variety of data sources. It can also solve problems under a variety of contexts.
For Instance, it can be used to add unique functionalities to enterprise systems that may otherwise be too difficult to program. We’re already using to solve large-scale process improvement initiatives to support business objectives for many industry-leading organizations. Programs like Six Sigma is already being replaced by many corporations, and they’re leaning towards training ML algorithms to enhance their business process.
Uncover hidden insights
It can handle nonspecific and unexpected situations. When organizations are uncertain about the value or insights inherent in their data — or are confronted with new information they don’t know how to interpret — ML can help discover business value where they may not have been able to before.
With all the benefits and capabilities, there are some challenges that become a roadblock for organizations in adopting ML in their Industry such as:
It requires considerable data and computing power as it applies analytics to such large amounts of data and runs such sophisticated algorithms, it typically requires high levels of computing performance and advanced data management capabilities. Organizations will need to invest in infrastructure to handle it or gain access to it through the on-demand services of external providers, such as big data analytics cloud providers.
It adds complexity to the organization’s data integration strategy. ML feeds off of large amounts of raw data, which often come from various sources. This brings a demand for advanced data integration tools and infrastructure, which must be addressed in a thorough data integration strategy.