Outcomes |
- Acquire a solid understanding of machine learning concepts, including supervised, unsupervised, and reinforcement learning techniques.
- Develop proficiency in Python programming, including using libraries like NumPy, Pandas, Scikit-Learn, and TensorFlow for data manipulation, analysis, and model building.
- Learn to preprocess data, engineer features, and evaluate models to optimize machine learning algorithms for real-world applications.
- Gain hands-on experience by working on real-world projects and capstone assignments, building a portfolio that demonstrates your ability to solve complex problems with machine learning.
- Obtain a Certificate of Completion, validating your skills in machine learning with Python and enhancing your career opportunities in AI, data science, and related fields.
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