Machine Learning

Crypto Trading Trends are ruled by AI and Machine Learning

We have reached a very important stage of blockchain technology with crypto trading. Currency traders are using machine learning tools to make smarter decisions on their blockchain investments. If you are interested in blockchain technology, getting certified with a machine learning AI course could prove to be highly beneficial in 2021.

Deep learning and AI have proliferated deep into every business domain. However, the most exciting area of application for AI ML capabilities is a fairly new digital currency exchange platform called cryptocurrency. You may have heard about a fairly popular word, called Blockchain. In many trading concepts around the world, cryptocurrency is used as an extended application of blockchain technology.

Before diving into crypto trading projects using the foundations of the Machine learning course in Delhi, let’s start with the basics.

Let’s first define cryptocurrency!

According to a leading technology review company, cryptocurrency is defined as a cryptographically secured digital currency or virtual currency. The purpose of developing cryptocurrency is to reduce middleman interference and prevent government and central regulatory bodies from issuing harsh transaction fees on every transaction. It also ensures cryptocurrency sellers and buyers have a fairly straightforward relationship that is built on accurate records, monitoring, and transparency. Cryptography prevents digital payments from fraud and manipulation.

However, despite popularity in the data market, crypto traders still have to face numerous challenges in their trading operations, of which exchange rate volatility and infrastructure vulnerabilities (read, personal data theft) are proving to be the biggest pain points in an otherwise very user-friendly industry.

How the use of AI and Machine Learning skyrocketed in crypto trading?

If you closely monitor the crypto trading ecosystem, you will definitely come across these currencies.

  • Ethereum (ETH)
  • Basic Attention Token (BAT)
  • Binance Coin (BNB)
  • Ripple (XRP)
  • Chainlink (LINK)
  • Stellar (XLM)
  • Polkadot (DOT)

Dogecoin (DOGE

Each of these is heavily backed by data science concepts and uses AI and Machine Learning applications in the most forwarding manner. In fact, highly specialized fintech analysts from top machine learning AI courses take up projects on how to determine crypto prices using predictive analytics.

The crypto trading pricing model is derived from the more traditional stock prices predictive analytics models. And, the similarities end here. The neo-banking regulations that control the stock price predictions are not applicable to crypto trading and that’s where AI ML models become weapons of mass adoption among crypto users. Due to the lack of reference indices and governing authorities for bitcoin and other cryptocurrencies, using AI ML for pricing models is an extremely challenging endeavor.

So, how to start with a machine learning AI course on crypto trading?

Like all AI ML projects, start with a data set that you can validate for your ML model. There are at least 50 different open source crypto data mining platforms online that provide information on:

  • Crypto average price
  • Opening stock price
  • Closing stock price
  • The volume of crypto coins on the trading market
  • Age of crypto

GitHub, Gitlab, Python, and R provide a basis for crypto trading analysts to start coding for real time crypto data matched with Pandas libraries.

Through your learning with a machine learning course in Delhi, you can bring into picture numerous data normalization techniques for the ML model.

What is Normalization? It’s the process of preparing data before it is fed to a Machine Learning algorithm. For crypto trading pricing prediction models, data normalization plays a very critical role and that’s why it is valued so much in any ML building project.

Distance based algorithms with K-Mean and K-NN are the most preferred data normalization techniques that are used for the standardization of entries. It is fairly easy to understand that you can actually build a sequential model for neural networks to speed up the ML model and reduce errors in prediction.

If you are willing to expand your horizon to the capital markets, your work on ML models with crypto pricing analytics would pay rich dividends. Open AI’s GPT-3 and Google BERT are already opening up new avenues for the digital payments market — and your ML algorithm could be the next big thing in the capital markets.

AI research labs at FACEBOOK, GOOGLE, Microsoft are investing billions of dollars in crypto mining, bringing AI and Machine Learning techniques to the center of adoption globally. Your next AI endeavor could take you to these companies where you can reap rich benefits from your crypto modeling experience.

Leave a Reply

Your email address will not be published. Required fields are marked *