Machine Learning

Machine Learning is the sub-branch of Artificial intelligence that enhances the learning power of the machines. Machines are fed with a huge amount of datasets that enables them to take a decision on their own without being explicitly programmed. Artificial intelligence machines require less very less human intervention for obtaining the results. Machine learning as a service has helped in obtaining the results for the businesses.

Classification of Machine Learning Algorithms

There are three types of machine learning algorithms:

1. Supervised Learning :
The supervised learning consists of outcome variables that are predicted from a universal set of predictors. Supervised learning variables generate a function that maps inputs to the required outputs. The training is continued until the algorithm reaches desired degree of precision. Various examples of supervised learning are KNN, regression, logistic regression etc. Python machine learning has helped in implementing the supervised learning algorithms with much accuracy.

2. Unsupervised Learning :
Unlike supervised learning, it does not have target or outcomes to predict. It just classifies the population into different sections. This helps in the customer segmentation. Unsupervised learning is the most commonly used machine learning algorithm in Python. Some examples are K-means and Apriori algorithm.

3. Reinforcement Learning :
Another Machine Learning algorithm that is widely used is reinforcement learning. It enables machines to take specific decisions. The machines are exposed to the environment where they can learn autonomously by hit and trial method. These have the ability to learn from experience also. Markov Decision Process is the best example. With the help of Python machine learning, reinforcement learning implementation has become easier.

Most popular machine learning algorithms :

Here is a list of some of the most popular machine learning algorithms:

1. Linear Regression
2. Logistic regression
3. Decision Tree
4. SVM
5. Naive Bayes
6. kNN
7. K-Means
8. Random Forest

Let us discuss some of the real life application of Machine Leaning :

1. Trading made simple and better :
One of the applications of Machine Learning as a service in the business's operations is that it is making trading simpler and better. The AI machine learning algorithms helps in implementing the rules, conditions, sentiments that makes trading simpler. In the current scenario, statisticians are building the trading algorithms. This will be replaced by machine learning algorithms that are powered by neural networks. These neural networks learn from the past behavior and get improved over time. Webtunix AI is a deep learning company in India that provides solutions to make the trading process simple for the companies.

2. Better risk management :
We are well aware that businesses have risk factor involved whether it be decision making or market prediction. Financial markets involve risks. By taking calculated risks, the chances of getting into loss is reduced. Moreover taking calculated risks can help the financial markets to grow steadily. Machine Learning as a service has helped financial markets by taking calculated risks. As ML can handle a huge amount of data at a particular time, it can analyze data much better than humans. Also, AI machines do not involve guesses in taking decisions. This also helps in managing the risk better. The machine learning algorithms build by Webtunix AI can minimize the risk factor and increase the sales to manifolds.

3. ML supported chatbots for clients support :
Another application of machine learning in the businesses is- better customer support. Chatbots are built using AI machine learning algorithms that are capable enough to understand, process and answer the queries of clients. Chatbots can handle multiple users at a given point in time. Chatbots are built to be friendly and they help in making a good relationship with the clients. Webtinix AI is a Deep Learning company providing customized chatbots to improve the user experience.

4. Machine Learning in Banking Domain :
Machine Learning software companies like Webtunix AI are helping the banking domain in the enormous number of ways. They help banking domain by providing powerful solutions to the everyday problems faced by the banking sector. Banking is all about data. Data is the main pillar of banking domain. AI-powered systems help banks in handling a huge amount of data efficiently.

For example, managing account operations, transactions, liquidity in the bank etc. It has also made the process of credit and loans simpler. Moreover, AI machines generate insights and identify the loopholes in the system that are hard for humans to identify.

5. Fraud identification :
Online payment, online money transfer has become an integral part of any business. But this also has given a chance for fraudsters to peek into their information and get access to the account. In the recent years, online fraud is gaining momentum.

Someone somewhere can have access to your account and perform illegal activities. Machine Learning as a serevice helps in reducing the risk of fraud. It has pieces of information about your location, past payments, amount of payments etc. If at some point in time it detects some deviation or breach in your account, it immediately alarms you or the concerned authorities in the bank. In some cases freezing your bank account making further transactions impossible. At Webtunix AI, we are building secure applications that are having multi-level security and can identify frauds at an early stage.

6. Stock Market prediction :
Deep learning companies like Webtunix AI has provided solutions for better stock market prediction. The traditional programs allow stock marketers to purchase the bonds when the stock market drops to a specific amount and to sell the bonds when it surpasses specific amount. By automating the process using Machine Learning applications, the process of trading has become simpler and effective. Machine Learning algorithms predict the trends of the market so that the fund managers can identify the market changes before they happen.

7. Blockchain :
Blockchain technology concept was first coined in 2009 with the introduction of cryptocurrency. It helped in decentralizing the cryptocurrencies. But this has now found application in the banking and financial markets also. It helps in making a record of the ledger, previous transactions that make the banking system transparent. Webtunix AI is providing solutions in various blockchain technologies also.

Machine learning as a service have changed the landscape of various operations in businesses. The blend of financial service with the machine learning has improved the level of service day by day. More and more financial organizations now want to deploy machine learning applications. Customers are gaining most from machine learning applications. As they can use face recognition systems to unlock the finance apps, biometrics, voice commands etc that makes the financial system secure. If you want your business to be incorporated with machine learning applications you can contact Webtunix AI team for long lasting and efficient solutions.