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Learn core ML concepts like supervised and unsupervised learning to build a strong foundation.
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Master Python and essential math like algebra and calculus to support your ML skills.
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Explore libraries like NumPy and Pandas to simplify data manipulation and ML tasks.
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Understand and implement key algorithms like linear regression and decision trees.
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Apply your knowledge to real projects or Kaggle challenges for hands-on experience.
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Enroll in structured ML courses on platforms like Coursera to gain in-depth knowledge.
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Stay updated and connect with other learners through blogs, forums, and webinars.