Top 10 Machine Learning Projects for Beginners

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It is always helpful to gain insights on how real people are beginning their career in machine learning. In this blog post, you will find out how beginners like you can make a great progress in applying machine learning to real-world problems with these fantastic machine learning projects for beginners recommended by industry experts. DeZyre industry experts have carefully curated the list of  top machine learning projects for beginners that cover the core aspects of machine learning such as supervised learning, unsupervised learning, deep learning and neural networks. In all these machine learning projects you will begin with real world datasets that are publicly available.  We assure you will find this blog absolutely interesting and worth reading because of all the things you can learn from here about the most popular machine learning projects.

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The Process Behind Beginning to Work on a Machine Learning Project

You want to start working on a machine learning project-but what processes do you need to remember and how you will manage the data are the two crucial stages to begin working on any machine learning project. Choose a dataset and understand it to decipher as to which machine learning algorithm class or type can address the business problem in the best possible manner.

For machine learning beginners, our experts suggest that they start with a modest sized dataset that has been well studied before by experts. There are multiple libraries of data sources  that have high-quality datasets such as – UCI ML Repository and Kaggle. Your goal before you begin any ML project should be to have an in-depth understanding of the problem that the data represents, the structure of the dataset and what all machine learning algorithms are best suited to solve the problem at hand. When studying the dataset , always use a statistical environment so that your focus remains on the questions you are looking to answer about the dataset instead of being distracted from a given technique and learning how to implement it in code. Some tips and tricks that can be of great help when studying and working with a machine learning dataset –

  • Have a clear and in-depth understanding of the problem that the dataset represents.
  • Always summarize the machine learning dataset using descriptive statistics.
  • Make a note of the structures you observe in the data and put forward all the relationships  observed in the data.
  • Quickly test a couple of top machine learning algorithms on the dataset and find out which general class of algorithms has a better performance.
  • Tune the algorithms to identify the algorithm that performs well for a given data problem and tune it accordingly.

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