Machine learning abilities are highly sought after in today's market, given that machine learning systems power the vast majority of financial trading operations on Wall Street as well as personalized recommendation features at tech giants, including Amazon, Spotify, and Netflix.
This comprehensive course launches with linear and logistic regression, which represent the foundational and most reliable approaches to solving machine learning problems. The curriculum progresses through algorithms with fundamentally different mathematical principles, including k-nearest neighbors, decision trees, and random forest methodologies. These more advanced techniques bring critical statistical ideas into focus, including understanding bias and variance in models, and recognizing overfitting. You'll develop skills in model assessment techniques, plus learn strategies for identifying and choosing the most effective input characteristics and algorithmic approaches.
The curriculum emphasizes practical implementation abilities essential for addressing real-world machine learning challenges. While mathematical principles supporting each algorithm will be illustrated visually, the course avoids heavy mathematical theory. It's expected that students joining the class have solid competency with Python programming, specifically the Numpy and Pandas scientific libraries.
Required Background Knowledge:
Students enrolling must demonstrate comfort and proficiency with Python and its data science tools (NumPy and Pandas). Those lacking Python experience should first complete our Python for Data Science Bootcamp before joining this course.
Topics and Skills Covered:
- Data cleaning and standardization using the Pandas library
- Implementation of machine learning algorithms, including logistic regression and random forest via scikit-learn
- Feature engineering and selection for optimal model inputs
- Partitioning data into training, validation, and test segments
- Understanding essential theory: model overfitting, variance, and bias components
- Assessment and validation of machine learning model performance
Explore more about Python Machine Learning Immersive at Practical Programming.
