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Best machine learning jobs for a promising career


The field of artificial intelligence has been growing rapidly over the last few years. In fact, in the next few years, artificial intelligence could become our best friend. Many experts predict that increased demand for this technology will mean more jobs and more opportunities for anyone with a good idea. However, finding a good job in artificial intelligence is an uphill task. To make things easier students are looking for the best machine learning python course. You have to be smart, careful and diligent to do so. We’ve been reading a lot of articles and trying different approaches to help you become successful in finding the best jobs in artificial intelligence.  So to ensure you make the right selection we have made a list of some of the best jobs in the field of AI that can guarantee a lucrative career.

1. Director of Analytics

The director of analytics is responsible for managing the overall performance and success of an organization’s data and analytics team. This position requires a strong understanding of business processes, data management strategy and implementation, as well as a proven track record in implementing large-scale data analytics projects. The director of analytics is in charge of the entire data science team, working to make sure all aspects of the business are being measured and analyzed.  This type of job requires extensive knowledge of statistics and mathematics as well as various software packages to work with data sets. 

This is because the person working in this position is responsible for all aspects of quantitative research, including data collection, cleaning, processing and analysis.  They regularly help choose and implement analytic solutions to solve complex problems. They are also responsible for hiring, training and managing their team as well as making sure they have access to the right tools.

2. Computer vision engineer

The computer vision engineer is responsible for developing algorithms that recognize objects, detect humans or animals, identify faces and other people or animals in images, and recognize text on documents or signs. This requires him or her to understand the principles behind machine learning models, which involve several steps like learning from examples, training the model with large data sets to learn how it works on new instances and then applying it against new data sets.

3. Data Science

Data scientists are the brains of the operation. They are responsible for developing algorithms that automate the work of data engineers, who build and maintain databases. Data scientists ensure that the data is clean and ready to be analysed, while data engineers ensure that it’s accessible by other programs and systems. Data scientists need experience in computer science and mathematics as well as extensive knowledge of statistics, machine learning and programming languages such as R or Python.

4. Data Engineer

Data engineers are responsible for creating, maintaining and updating databases used by other applications within the company. For example, if a company has an application that needs to access a database of customer records, a data engineer is likely responsible for building the necessary connection between the two systems.

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