How can I start learning machine learning from scratch?

To start learning machine learning from scratch, I would recommend the following steps:
Gain a solid foundation in mathematics, particularly linear algebra, calculus, and statistics. Machine learning relies heavily on these mathematical concepts, so having a strong grasp of them is crucial.
Learn the fundamentals of programming, preferably in a language commonly used for machine learning, such as Python, R, or Java. Familiarize yourself with data structures, algorithms, and basic programming constructs.
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Explore the core machine learning algorithms and techniques. Start with supervised learning algorithms like linear regression, logistic regression, decision trees, and support vector machines. Then move on to unsupervised learning algorithms like kmeans clustering and principal component analysis.
Understand the various types of machine learning problems, such as classification, regression, clustering, and reinforcement learning. Learn how to formulate realworld problems as machine learning tasks.
Familiarize yourself with popular machine learning libraries and frameworks, such as TensorFlow, Keras, PyTorch, or scikitlearn. These tools will allow you to implement and experiment with machine learning algorithms more easily.
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Practice, practice, practice. Apply the concepts you’ve learned to realworld datasets, participate in machine learning competitions on platforms like Kaggle, and work on personal projects to solidify your understanding.
Stay uptodate with the latest developments in the field by reading research papers, following machine learning blogs and communities, and attending workshops or conferences (if possible).
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