Machine learning can only begin if the base foundation in core concepts and programming skills is there. For this purpose, the necessary knowledge in essentials such as supervised and unsupervised learning, classification, regression, and model eval...
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Machine learning can only begin if the base foundation in core concepts and programming skills is there. For this purpose, the necessary knowledge in essentials such as supervised and unsupervised learning, classification, regression, and model evaluation should be known, thus equipping one to get into deep levels. Programming languages, for instance, like Python and related libraries, NumPy, Pandas, and TensorFlow are key elements to put practical aspects to test. Taking online courses and having hands-on projects, such as those on Kaggle, will help you apply this knowledge to real-world practice. Once you feel relatively comfortable with basic ML topics, you can explore and dive into advanced topics, such as deep learning, to expand your expertise even further. The key for success in machine learning is continuous learning and practice, keeping you abreast of new techniques while gaining experience through diverse projects.
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