Programming Foundations of Classification and Regression (Machine Learning with Python series)

Colleagues, this video-based training program includes over four hours of Code-along sessions move you from introductory machine learning concepts to concrete code. Machine learning is moving from futuristic AI projects to data analysis on your desk. Code-along sessions move you from introductory machine learning concepts to concrete code. Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond nodding along in discussion to coding machine learning tasks. These videos show you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends.You will learn how to Build and apply simple classification and regression models, Evaluate learning performance with train-test splits, Evaluate learning performance with metrics tailored to classification and regression, and Evaluate the resource usage of your learning models. Core skills-based training modules will equip you with: 1) Software Background (Numpy, Matplotlib, scikit-learn, seaborn, and pandas–high-level packages), 2) Mathematical Background, 3) Beginning Classification (Part I): Two models: k-nearest neighbors and naive Bayes, 4) Beginning Classification (Part II): Evaluate learning performance with accuracy and how to evaluate resource utilization for memory and time within Jupyter notebooks and also in standalone Python scripts, and 5) Beginning Regression (Parts I and II).

Register today at: https://tinyurl.com/y77nvs7m

Career success awaits you, Lawrence Wilson – Artificial Intelligence Academy