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When you’re first starting out, try examining and recreating basic projects provided by Scikit-learn, Awesome Machine Learning, PredictionIO, and similar resources. Once you have a solid grasp on how machine learning works in practice, try coming up with your own projects that you can share online or list on a resume.  So you don’t have to spend time collecting data, try using publicly available data sets from places like the UCI Machine Learning Repository and Quandl.  If you can’t come up with a project idea, look for inspiration on websites like GitHub. Kaggle is a dataset database that hosts a variety of machine learning challenges. Some of these are official competitions, which offer monetary prizes, and some are free competitions that simply provide experience. To start out, try completing the beginner competition Titanic: Machine Learning from Disaster. While personal projects and competitions are fun and look great on a resume, they may not teach you the business-specific machine learning skills required by many companies. So you can gain this experience, look for internships or entry-level jobs related to product-focused machine learning. Look for relevant internships on websites like Internships.com.
Work on personal machine learning projects. Participate in Kaggle knowledge competitions. Apply for a machine learning internship.