Machine Learning Algorithms - Data scientist may be the sexiest job today but the understanding, implementation, applied ML experience is missing. In real business having top algorithms at your fingertips is wasting big time. The real job for any data scientist is the ability to articulate, demonstrate, extract, and reap the rewards of real values ​​from data. "Machine learning" as a basic skill sounds like a teleportation tool for many businesses, especially for companies that are actually data factories, i.e. social media platforms. The main idea of ​​this post is to describe and illustrate the top few machine learning algorithms.

Data Scientists Basic Tool Kit

Data scientists must be able to work with their basic skills to gain insight into data, impact on business, and work with non-analytical resources. It is more important to answer the questions of businessmen at the grassroots level than answering them from the perspective of PhD Scholar Books Materials. In this post, the main idea is to introduce you to the main algorithms that should be in a data scientist's toolbox. Some popular machine learning algorithms.



The data scientist should be bilingual in the context of day to day business to provide an overview of

How the data organization collects and conceptualizes the ideas behind the data that can transform the data into actionable knowledge.
answer business questions
Facilitate the tools data analysts, data scientists and business teams work with.
Tell the business what's in the data and learn about the business's goals and objectives to hunt down related information in the collected data. The Data Scientist Tool Kit should be able to paint a great primer for what Data Science is about. The point to note here is that AI is much more than ML. I especially think that knowing the types of mlalgo really helps to see a somewhat clearer picture of AI. Answer to the question "Which machine learning algorithm should I use?" 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.

For us, at AILabPage we say that machine learning is crystal clear and eating ice cream is a task. It is not only for PhD candidates but it is for you, us and everyone.

Machine Learning and AI

The terms artificial intelligence and machine learning are often used interchangeably but they are not the same. Machine learning is one of the most active areas and one way to achieve AI. Why ML is so good today; For this, there are some reasons like but not limited to below.

big data explosion
Hunger for new business and revenue streams in this business in shrinking times
Advances in Machine Learning Algorithms
Development of extremely powerful machine with high capacity and fast computing capability
storage capacity
Today's machines are learning and working; This was done only by humans in the past like making better decisions, making decisions, playing games etc. This is possible because machines can now analyze and read through patterns and remember the learning for future use. The biggest problem today is to find resources that are skilled enough to showcase and differentiate your education from university and PhD books in real business rather than arguing with others on social media.

Machine learning should be considered as a culture in an organization where business teams, managers and executives must have some basic knowledge of this technology. To achieve this as a culture, there will be constant programs and road shows for them. There are a number of courses that are designed for students, employees with little or no experience, managers, professionals and executives to give them a better understanding of how to use this great 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 understand what is already hidden before us in our data. Machine learning and its algorithms are either supervised or unsupervised, but the future really lies in reinforcement learning.

In deep learning, the concept of using multiple layers of non-linear processing units for feature extraction and transformation extends the boundaries of Machine learning. Artificial neural network structure is what enables artificial intelligence, machine learning and supercomputing to flourish. Neural networks are powering language translation, facial recognition, picture captioning, text summarization and more.

In short, the success of an algorithm depends on how the math of the algorithm was translated into the instructions for the computer you were using. And it depends on how much time you have.

Regression Analysis


Linear regression - simple linear regression - has only one independent variable. Multiple Linear Regression – refers to defining the relationship between the independent and dependent variables
Logistic Regression - A super simple form of regression analysis in which the outcome variable is binary or dichotomous. Helps estimate prevalence rates, adjusted for potential confounders (socio-demographic or clinical characteristics)

Decision trees


Classification and regression trees are an important type of algorithms for prediction modeling machine learning. A greedy algorithm based on the rule of divide and rule. Split records based on a trait test that optimizes certain criteria. The real value is in determining how to split the records.

Naive Bayes


Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between features.

K-Nearest Neighbours


The laziest algorithm which is a very simple algorithm that stores all the available cases and predicts the numerical target based on the similarity measure. As a non-parametric technique in the early 1970s, KNN has already been used in statistical inference and pattern recognition.

Generative Adversarial Networks

A very young family member of Deep Neural Network Architecture. Presented by Ian Good-Fellow and his team at the University of Montreal in 2014. GANs are a class of unsupervised machine learning algorithms. Hence as the name suggests it is called Adversarial network because it is made up of two neural networks. Both the neural networks are assigned different job roles i.e. contesting with each other.

A neural network is called a generator because it generates new data instances.

The other neural net, called the discriminator, evaluates the function of the first neural net for authenticity.

The cycle continues to yield results near accuracy or perfection. Still confused, it's ok to read this post on "Generative Adversarial Networks"; You will get more details and understanding.

Recurrent Neural Networks

Recurrent Neural Network - This is called a deep tree-like structure. These neural networks are used to understand context in speech, text or music. RNN allows information to loop through the network. The tree-like topology allows branching connections and hierarchical structures. Data flow in RNN occurs in multiple directions. These networks are employed for highly complex tasks such as voice recognition, handwriting and language recognition etc.

The capabilities of an RNN are quite limitless. Don't get lost between recursive and recurrent NN. The structure of an ANN is what enables artificial intelligence, machine learning, and supercomputing to flourish. Neural networks are used for functions such as language translation, face recognition, picture captioning, text summarization and much more.

Convolutional Neural Networks

Convolutional Neural Networks (CNN) is an excellent tool and one of the most advanced achievements in deep learning. CNN has received a lot of attention and attention from all the major business players because of the promotion of AI. The two main concepts of convolutional neural networks are convolution (hence the name) and pooling. It does this work on the backend with multiple layers transferring information from one sequence to another.

Human brain detects any image in fractions of seconds without any effort but computer vision image is really just an array of numbers. In that array, each cell value represents the pixel brightness from black to white for the black-and-white image. Why do we need CNN and not just use feed-forward neural network? How can capsule networks be used to overcome the drawbacks of CNN? etc. I think if you read this post on "Conventional Neural Networks"; You will know the answer.