FRONT OF CARD

Front text

Extracted from page 197.

1. Understanding of various machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
2. Proficiency in using machine learning libraries and frameworks, such as scikit-learn, TensorFlow, or PyTorch.
3. Knowledge of data preprocessing techniques, feature engineering, and dimensionality reduction methods to enhance model performance.
4. Understanding of model evaluation metrics and techniques to assess the performance and reliability of machine learning models.
5. Ability to address issues related to overfitting, underfitting, and bias-variance trade-offs in machine learning models.
6. Knowledge of advanced machine learning concepts, such as ensemble methods, deep learning, or transfer learning, and their practical applications.

IMPACT P R O F I C I E N T W I T H M A C H I N E L E A R N I N G T E C H N I Q U E S

Better The Deck

CRAFT (MIMTSK) CHARACTER (WIRE) CAPACITY (ME) DRIVE (TV) =

BACK OF CARD

Back text

Extracted from page 198.

1. Can you share a project where you used supervised learning? What problem did it solve, and why was this approach suitable?
2. How do you choose between unsupervised and supervised learning for a new AI problem? Can you share an experience when choosing one significantly impacted the outcome?
3. Could you share an experience applying reinforcement learning to an AI problem? Why did you choose this method?
4. Have you used multiple machine learning algorithms in a project? How did you combine these approaches for a better solution?
5. How do you stay updated on new machine learning algorithms and techniques, and how has this influenced a recent project?

COMPETENCIES FOR WORKING WITH ARTIFICIAL INTELLIGENCE – MACHINE LEARNING P R O F I C I E N T W I T H M A C H I N E L E A R N I N G T E C H N I Q U E S

Better The Deck

REFER TO COACHING ROUND THREE INSTRUCTIONS