What you would learn in Build 30 Real World Machine Learning Projects Practically course?
Traffic alerts, video surveillance on your mobile, facial recognition software personal product recommendations while browsing websites; are just a few examples of existing machine learning software.
This vital area, a subdiscipline within artificial intelligence, has been attracting lots of attention in recent times due to its technological advancements and lucrative career possibilities. The employment website Indeed has ranked the machine learning engineer as number 1 among the top jobs across the U.S., citing an increase of 344% and a median annual salary of $146,085 annually.
Computer IT and related jobs are growing, with projected employment to increase by 11% between 2019 and 2029. In particular, it is predicted that the Future of Jobs Report 2020 forecasts that artificial Intelligence will result in 12 million jobs in 26 countries in 2025 (representing an increase of 97 percent of 97 million new jobs will be created and 85 million jobs lost).
There is a high demand for AI and machine-learning expertise leading to high wages. Most machine learning jobs pay over $100,000, with some like machine learning engineers earning as high as $200,000.
There are a variety of experts in machine learning, and their responsibilities can overlap with other positions by the size of the organization, such as Machine Learning Engineers, Data Science Engineer, Data scientists, ML Developer, etc. The general rule is that machine learning is concerned with complicated problems.
In this class, we are going to be working to work on Building a Machine Learning Project that focuses on Car Price Prediction.
How to Finish the Machine Learning Project are
Data Collection: As you are aware, machines begin to learn from the data you offer them.
Preparing the Data: Once you have the data, you will need to make it available for analysis.
Selecting a Model
Training the Model
Assessing the Model
Learning how to design machine learning-related projects
Know the data analysis process
Learn to do exploratory data analysis
Learn how to make machine learning models.
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