Machine Learning Algorithms – At present, data science is held in high regard, but there is a lack of proficiency in comprehending, executing, and gaining hands-on experience with machine learning. Not having the chance to use top-notch algorithms in real-world business practices is a major drawback. It is essential for a data scientist to have the ability to interpret, demonstrate, and derive actionable information from data in order to achieve beneficial results.
Learning Machine Learning skills is widely seen as a game-changing advantage for organizations, especially those with data-driven operations, as it has the potential to provide significant benefits. Nowadays, the most common term used to describe digital communication tools is social media platforms. The main aim of this written communication is to explicate and exemplify the prominent machine learning algorithms.
Data Scientists Basic Tool Kit
Data scientists with his/her basic skill should be able to communicate insights of data, impact on business and able to work with non-analytics resources. Answering questions from business people at the ground level is more important rather answering from PhD scholar books material perspective. In this post, the main idea is to introduce you to the main algorithms that should be in the data scientist’s toolbox. Some of the popular machine learning algorithms.
The data scientist should be bilingual in terms of day to day business in order to provide an overview of
- The data organisation collect & how to conceptualise the ideas behind data that can turn data into actionable knowledge.
- Answer the questions business have
- Facilitate the tools that data analysts, data scientists and business teams should work with.
Let the business know what’s in the data and learn about the goals and objectives of the business to hunt down related information in the collected data. A data scientist’s tool kit should be able to picturise a great primer for what Data Science is about. The point to note here is that AI is much more than ML. I particularly think that getting to know the types of MLAlgos actually helps to see a somewhat clear picture of AI. The answer to the question “What machine learning algorithm should I use?” is always “It depends.” It depends on the size, quality, and nature of the data. It depends on what you want to do with the answer.
To us, at AILabPage we say machine learning is crystal clear and ice cream eating task. It is not only for PhDs aspirants but it’s for you, us and everyone.
Machine Learning and AI
Artificial intelligence and machine learning are used interchangeably often but they are not the same. Machine learning is one of the most active areas and a way to achieve AI. Why ML is so good today; for this, there are a couple of reasons below but not limited to though.
- The explosion of big data
- Hunger for new business and revenue streams in this business shrinking times
- Advancements in machine learning algorithms
- Development of extremely powerful machines with high capacity & faster computing ability
- Storage capacity
Today’s machines are learning and performing tasks; that was only done by humans in the past like making better judgment, decisions, playing games, etc. This is possible because machines can now analyse and read through patterns and remember learnings for future use. Today the major problem is to find resources that are skilled enough to demonstrate & differentiate their learning from university & PhD books in real business rather than just arguing on social media with others.
Machine learning should be treated as a culture in an organisation where business teams, managers and executives should have some basic knowledge of this technology. To achieve this as a culture, there have to be continuous programs and roadshows for them. There are many courses which are designed for students, employees with little or no experience, managers, professionals and executives to give them a better understanding of how to harness this magnificent technology in their business.
Top Machine Learning Algorithms
Data science is neither magic nor rocket science, it does not create or invent any new information or facts. Data science helps us to make some sense of what’s already in front of us hidden in our data. Machine learning and its algorithms are either supervised or unsupervised as of today but the future really lies in reinforcement learning.
In deep learning, the concept of using many layers of nonlinear processing units for feature extraction and transformation extends machine learning boundaries. The artificial neural network structure is what enables artificial intelligence, machine learning and supercomputing to flourish. Neural networks are powering language translation, face recognition, picture captioning, text summarisation and a lot more.
In short, the success of an algorithm depends upon how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have.
- Linear Regression – Simple Linear Regression- there is only an independent variable. Multiple Linear Regression- refers to defining a relationship between independent and dependent variables
- Logistic Regression – A super simple form of regression analysis in which the outcome variable is binary or dichotomous. Helps to estimate adjusted prevalence rates, adjusted for potential confounders (socio-demographic or clinical characteristics)
Classification and regression trees are an important type of algorithm for predictive modeling machine learning. A greedy algorithm based on divide and conquer rule. Split the records based on an attribute test that optimises certain criteria. The real value is in determining how to split the records.
Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between the features.
The laziest algorithm which is also a very simple algorithm that stores all available cases and predicts the numerical target based on a similarity measure. In the beginning of 1970s as a non-parametric technique, KNN has been used in statistical estimation and pattern recognition already.
Generative Adversarial Networks
A very young family member of Deep Neural Networks Architecture. Introduce by Ian Good-fellow and his team at the University of Montreal in 2014. GANs are a class of unsupervised machine learning algorithms. So as the name suggests it is called Adversarial Networks because this is made up of two neural networks. Both neural networks are assigned different job role i.e. contesting with each other.
Neural Network one is called as the Generator because it generates new data instances.
Other neural net is called the Discriminator, evaluates work for the first neural net for authenticity.
The cycle continues to obtain accuracy or near perfection results. Still confused, it’s ok to read this post on “Generative Adversarial Networks“; you will find more details and understanding.
Recurrent Neural Networks
Recurrent Neural Networks – Call it a deep tree-like structure. These neural networks are used to understand the context of speech, text or music. The RNN allows information to loop through the network. Tree-like topology allows branching connections and hierarchical structure. In RNNs data flow is in multiple directions. These networks are employed for highly complex tasks i.e. voice recognition, handwriting and language recognition, etc.
RNNs’ abilities are quite limitless. Don’t get lost between Recursive and Recurrent NNs. ANN’s structure is what enables artificial intelligence, machine learning, and supercomputing to flourish. Neural networks are used for language translation, face recognition, picture captioning, text summarisation and a lot more tasks.
Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are an excellent tool and one of the most advanced achievements in deep learning. CNN got too much attention and focus from all major business players because of the hype of AI. The two core concepts of convolutional neural networks are convolution (hence the name) and pooling. It does this job at the backend with many layers transferring information in a sequence from one to another.
The human brain detects any image in fractions of seconds without much effort but in computer vision, the image is really just an array of numbers. In that array, each cell value represents the brightness of the pixel from black to white for a black-and-white image. Why do we need CNNs and not just use feed-forward neural networks? How capsule networks can be used to overcome the shortcoming of CNNs? etc. I guess if you read this post on “Convolutional Neural Networks“; you will find out the answer.
Recursive Neural Networks
Call it a deep tree-like structure. When the need is to parse a whole sentence we use a recursive neural network. Tree-like topology allows branching connections and hierarchical structure. Arguments here can be how recursive neural networks are different from recurrent neural networks?
Questions – How recursive neural networks are different than recurrent neural networks?
Answer – Recurrent neural networks are in fact recursive neural networks with a particular structure: that of a linear chain.
RNNs are hierarchical kinds of networks with no time aspect to the input sequence but the input has to be processed hierarchically in a tree fashion.
Books Referred & Other material referred
- Open Internet reading and research work
- AILabPage (group of self-taught engineers) members hands-on lab work.
Points to Note:
When to use artificial neural networks as opposed to traditional machine learning algorithms is a complex one to answer. Neural network architecture and its algorithms may look different to many people but in the end, there is nothing wrong to have them in your tool kit. It entirely depends upon the problem at hand to solve. One needs to be patient and experienced enough to have the correct answer. All credits if any remain on the original contributor only. The next upcoming post will talk about Recursive Neural Networks in detail.
Feedback & Further Question
Do you have any questions about Quantum technologies, Artificial Intelligence and its subdomains like Deep Learning or Machine Learning? etc. Leave a comment or ask your question via email. Will try my best to answer it.
Conclusion – For any effective machine learning model requirement is only one which is reliable data pipelines. I particularly think that getting to know the types of machine learning algorithms actually helps to see a somewhat clear picture. The answer to the question “What machine learning algorithm should I use?” is always “It depends.” It depends on the size, quality, and nature of the data.
Also, what is the objective/motive of data torturing? As more we torture data more useful information comes out. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Only recently machine learning got the spotlight and attention from the industry. Machine learning use cases like face recognition, image captioning, voice & text processing, and self-driving cars now everyone talks about.
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