The one difference between a man and his machines has always been the decision-making ability possessed by the living species. Machines on other grounds have surpassed humans such as intelligence, speed and memory but in the end, it would be a human who would approve of the machine’s work.
Enter Machine learning tools; these are tools that allow the machines to make predictions about data on its own using the previous usage of the same data. This power allows the machines to bridge their gap between them and humans.
Machine Learning is the practice of using ML algorithms to decipher data and learn from it, and then make predictions using the acquired knowledge. It is the process of making the machines efficient enough so that it becomes ‘artificially intelligent’ and processes the given data just by keeping previous tasks in its memory without using any extensive coding routines.
Until the introduction of AI, the coders mostly steered the task of churning hand-written classifiers for the machines to make sense out of data.
The algorithms were good and made the processing uncomplicated but there’s a reason computer vision and image detection didn’t come close to rivalling humans until very recently, it was too brittle and too prone to error.
The basic motive of machine learning is to give training data to a learning algorithm. The learning algorithm then generates a new set of rules, based on inferences from the given data. This is in essence generating a new algorithm, earlier referred to as the machine learning model.
By using diverse training data, the same learning algorithm could be used to generate different models. For example, the same type of learning algorithm could be used to teach the computer how to translate pictures into speeches or predict the stock market.
Machine learning is not a new concept. Many of the learning techniques are based on decade long research. The current growth in AI and machine learning relies on developments in three important areas:
Data Availability- As the consumption of data has increased leaps and bounds and combined with the decreased costs of storage, it forms a perfect breeding ground for machines to learn algorithms using this training data.
Powerful computers- With the new advanced computers coming into play, it has made the processing of enormous data seem like a piece of cake.
Algorithmic innovation- New machine learning techniques, specifically in layered neural networks termed as “deep learning” have inspired new services, but are also igniting investments and research in other parts of the field.
Algorithms play a very predominant role in learning. There are four sorts of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforced:
Supervised algorithms are machine learning tools that act as students as they are supervised in the learning process till the algorithms become self-sufficient.
Unsupervised algorithms require little or no human interaction as they use an approach called “deep learning” to review data and reach conclusions supporting previous samples of training data.
Semi-supervised algorithms tend to fall within the middle ground and are a combination of both supervised and unsupervised algorithms.
Reinforced algorithms force models to repeat a process until it produces the foremost favourable outcomes. Attempts that produce these favourable outcomes are rewarded and the ones that produce unfavourable results are penalized until the algorithm learns the optimal process.
Now just to make this concept crystal clear, here are few real-life examples of Machine Learning:
Example 1- If you have used Netflix, then you would be aware that it recommends you some movies or shows for watching based on what you have watched earlier. Machine Learning is used for this practice and to select the data which matches your choice.
Example 2- The second example is Software-processing, which shows how you will look when you get older. This image processing also uses the expertise of machine learning.
For a broader view read the following: Types of Machine learning
Machine learning tools are algorithmic applications of AI that give systems the power to find out and improve without extra human input; similar concepts are data processing and predictive modelling. They allow software to become more precise in predicting outcomes without being explicitly programmed.
The idea is that a model or algorithm is appointed to gather data from the planet, which data is reversed back to the model in order that it improves over time. It’s called machine learning because the model “learns” because it is fed more and more data.
They can be used, for instance, to create recommendation engines, predict search patterns, filter spam, build news feeds, detect threats and security frauds.
Machine learning Tools
Scikit-learn is used for machine learning development in python. It provides a library for the Python programming language and is built upon some of the technology, namely, NumPy, pandas, SciPy and Matplotlib.
The name Scikit comes from SciPy as modules of SciPy are known as scikit and the modules provide learning algorithms, hence the word ``learn''.
Although the interface is Python, c-libraries are utilized for performance such as NumPy for arrays and matrix operations, LAPACK, LibSVM and the careful use of cython.
Scikit-learn interface, Source
PyTorch is an open source machine library based on the torch library. The torch covers the basics of computing framework, scripting language, and machine learning library.
(Read also: NLP libraries)
PyTorch emphasizes flexibility and allows deep learning models to be expressed in Python. PyTorch supports strong GPU acceleration better than Numpy and also supports dynamic computation graphics.
Retrieved from PyTorch Github
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that allows researchers to push the state-of-the-art in ML and developers to easily build and deploy ML powered applications.
To give a concrete example, Google users experience a faster and more refined search with AI. If the user types a keyword in the search bar, Google usually provides a recommendation about what the next word could be.
A sketch to face example using TensorFlow:
Face detection using TensorFlow, Source: Github
Waikato Environment for Knowledge Analysis (WEKA), developed at the University of Waikato, New Zealand, is a free software licensed under the GNU General Public License for data mining.
These algorithms can either be applied directly to a dataset or called from your own Java code. It is also well-suited for developing new machine learning schemes.
Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It can be considered more of an educational tool rather than an industrial tool because it provides a great learning path for beginners.
Weka explorer interface with iris dataset loaded, Source
It is a learning tool for data analytics, reporting and integration platforms. Using the data pipelining concept, it combines different components of machine learning and data mining.
It has machine learning components built in and has associations with Weka which provides machine learning algorithms to the system. The workflows can run through both the interactive interface and also in batch mode. These two setups allow for easy local job management and simple process execution.
Logging in to the public EXAMPLES server in KNIME, Source
Intuitive interface- Modern ML tools provide an intuitive interface for the smooth working of the applied machine learning process. There is good mapping and a sense of comfort in the interface for the task.
Best processing- ML tools embody best practices for the process, configuration and implementation. Examples include automatic configuration of machine learning algorithms and good processes built into the structure of the tool.
Trusted resource- ML tools are well maintained, updated frequently and have a community of resources around it. Activity around a tool serves as a sign of it being used regularly.
Quick results- In the starting phase, such tools guide you through the process of delivering valuable results quickly and give you confidence and more to spend on your future projects.
Smart work- ML can review large volumes of data and discover specific trends and patterns that would not be visible to the human eye. In addition, this work could be completed in minutes, making the working smarter in every sense.
Minimal Human input- ML does not require any babysitting and hence open horizons for machines to learn, make predictions and improve their algorithms on their own.
Efficiency- With modern-day machines coming to the foreground, the quest for accuracy and efficiency increases day by day as the intricate algorithms keep gaining accuracy by each try and outdo their previous results. Aiming for better and higher has become the norm of this era- be it the man or his machines.
Wide applications- The dynamism within ML allows it to be used in different sectors. It holds the capability to help deliver a much more personal experience to customers while also keeping the target audience in mind.
(Similar read: Big data analytics tools)
We can safely conclude with this article that Machine Learning tools have made our lives seem pause-free as every important task is a touch and seconds away. These tools provide a user-friendly interface to tasks which may seem humongous and arduous.
A decade earlier who would have believed that machines would be able to perform at such high-performance rates and that too on its own but with a man’s intelligence and a machine’s artificial intelligence such feats are amassed every day.
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