When you start searching for something on Google. You start getting prompts of the keyword while you’re keying in your query. For example if you have typed “how to” you’re unlikely to get any random suggestion. For example if you have been searching for travel related data. You might be prompted to search for how to do web check-in.
If you don’t have a history of looking for health-related information. You are unlikely to be prompted to search for how to lose weight in a week. if you look for
something on Google, on coming to Facebook. You’re likely to find page or even recommendations on the topic. Now of course you wouldn’t expect a human being employed by Google or Facebook. Who is interested with the responsibility to track your movement on the internet and hardcourt prompts on recommendations. So who does it? Then the system itself empowered with the ability to learn on its own which is how machine learning works a core sub area of artificial intelligence.
Machine learning enables computers to use new data independently without being programmed. For each instance when exposed to new data these computer programs are unable to learn. Grow change and Develop by themselves where else is the concept of machine learning used in consumer applications.
Real-time ads on webpages and mobile apps, emails spam filtering, Network intrusion detection and pattern and image recognition by using machine learning. To analyze huge volumes of data in enterprise application. Customer relationship management (CRM). They analyze email and prompt sales team members to address the most important messages. First more advanced systems exist that can even suggest potentially effective responses business. Intelligence systems RBI use machine learning to automatically identify potentially important data points in human resources or HR.
Machine learning is used to identify characteristics of effective employees and use this knowledge to search for the best applicants for open positions. Machine learning recommends real-time smart alternatives to analyze huge volumes of heterogeneous data.
Now that you’re getting introduced to ml. Let’s define what you can expect to achieve in this module. You will get a basic understanding of machine learning. Understand the types of machine learning, understand the concepts of classification and regression and in order to do this. You need to write code in python using scikit-learn library. So, you will get an overview of scikit-learn library. You’ll be able to apply linear regression to business cases. Also logistic regression and decision tree. Then you will also learn about support vector machines and how to apply them in business cases.
Finally you’ll understand how to deploy machine learning models. After training them which is a critical piece of the whole process.