Fundamentals of Machine Learning: Understand the basics of supervised, unsupervised, and reinforcement learning.
Python for Data Science: Explore Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.
Data Preprocessing and Visualization: Learn how to clean, analyze, and visualize data for optimal model performance.
Building Machine Learning Models: Develop and evaluate regression, classification, and clustering algorithms.
Deep Learning Basics: Get introduced to neural networks using frameworks like TensorFlow or PyTorch.
Real-World Projects: Work on hands-on projects to apply your knowledge and build a portfolio.
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