Python is essential for those looking to get into machine learning and artificial intelligence. It is one of the easiest languages to learn and its range of dynamic semantics is unparalleled. It is easy to read and has reduced the cost of program maintenance. Artificial intelligence allows computers and software to ‘learn’ and identify patterns in order to predict outcomes and make conclusions without human interference or supervision. An example of this is the auto-reply feature on Gmail which ‘reads’ emails and predicts the reply. A machine learning engineer develops intelligent algorithms using data that has to be collected, assembled, and arranged first.
Learning Python is not just important, it is essential to machine learning and AI. There are several courses available online where you can get a Python certification and you should pick one that suits your level of expertise. If you are an absolute beginner, you should choose a course that will help you master the basics of Python. You will also learn how to use popular scientific libraries that support Python users.
The next step involves learning about Python in the scientific computing environment. As a machine learning engineer, one of your main tasks will be to work with large amounts of data. Python allows for intricate statistical modeling of said data. It works well with other programs and tools and allows for a wide range of interaction across different players.
An important area with Python learning is classification. Engineers have to be able to develop a model that classifies, identifies, and describes data classes in order to be able to classify unknown data in the future. It is one of the main forms of supervised learning and is an essential tool in your development of AI. Different types of classifier models include support vector machines, logistic regression, neural networks, and decision trees.
Regression is just as useful as classification and it also is an important form of supervised learning. However, unlike classification where there are distinct finite classes, regression works with predicting continuous numerical data.
When you are faced with data that does not have pre-defined classes, then your best tool is clustering. Simply put, clustering puts together data that are similar and separates the ones that differ. This type of data pooling is a form of unsupervised learning.
One of the best ways to learn the different aspects of Python is to learn by doing. There are several places online where you can practice your knowledge. You can also connect with other engineers and programmers and join a community to discuss and learn from others. Kaggle exercises and competitions are recommended to beginners who are looking for a challenge to flex their theoretical skills.
For those who are serious about machine learning, joining a reputed machine learning course will set you on the right path. The right machine learning training is intensive and allows you to learn hands-on with live projects. However, it is still recommended that you have some previous knowledge about Python, math, and statistics before venturing into these intensive courses.
Tag: Artificial Intelligence Training
How AI is Helping the Financial Sector Cover Regulatory and Compliance?
Synopsis
Artificial intelligence (AI) is here and is making waves in the financial industry. From sales management to compliance and protection against cybercrime, here is everything you need to know about AI
On any given day, you as a consumer can carry out transactions online without having to worry about security and if your payment goes through or not. How is this possible? From shopping online to overseas transfer, emerging tech such as Artificial Intelligence, Blockchain, Cloud has revolutionized the way the Financial industry works. In the past decade, Fintech has seen new dawn with many organizations heavily investing in Artificial Intelligence.
So, what is Artificial Intelligence? Simply put Artificial Intelligence courses are the ability of a machine to learn and process data for insights that impact the business.
It means that a machine is capable of learning on its own and arriving at solutions that can reduce cost and improve the efficiency of any business. In the financial sector, artificial intelligence is involved in every component today. From regulatory compliance to consumer insights, AI is changing the way the Fintech industry functions.
One of the most important aspects of the financial industry is regulatory compliance and cybersecurity. Another facet of this is sales management. As there is a shift in the way things work, it is important for the leaders of organizations to take stock of the benefits and consequences of deploying AI in their company.
Here are the top things that one must be prudent of while hailing in this new technology
Regulatory Compliance
Before Artificial Intelligence, the burden of compliance and authority rested with individuals and professionals who were trained in the field. This also accounted for human errors, incorrect processing of data, and took a longer
duration of time. With AI, there is minimal human intervention when it comes to regulatory compliance and the machine also takes less time when it comes to analyzing the right data and arriving at a solution.
This will also impact the business drastically and reduce costs. In the financial sector, compliance is something that cannot be compromised on, and thereby use of AI will have a positive impact.