Build Machine Learning Models Practice Exam

Build Machine Learning Models Practice Exam

About Build Machine Learning Models Exam

The Build Machine Learning Models exam is a comprehensive evaluation designed to assess your skills in the development, training, evaluation, and deployment of machine learning models. This exam is ideal for individuals seeking to validate their expertise in creating machine learning solutions and applying them effectively to real-world problems. It covers the entire machine learning lifecycle, from data preprocessing to model deployment, and is geared towards professionals in the data science and machine learning domains.

Skills Required

• Ability to collect, clean, and preprocess data for machine learning tasks.
• Skills in handling missing data, scaling, normalizing, and encoding features to prepare datasets for model training.
• Familiarity with various data formats and types, including time-series data, structured, unstructured, and semi-structured data.
• Proficiency in selecting the most appropriate machine learning algorithms based on the nature of the data and problem.
• Hands-on experience in applying supervised, unsupervised, and reinforcement learning techniques to solve problems.
• Knowledge of model types such as regression, classification, clustering, and deep learning.
• Ability to build models using popular machine learning libraries and frameworks like TensorFlow, PyTorch, Scikit-Learn, and Keras.
• Experience with splitting datasets into training and testing sets.
• Understanding of various metrics (e.g., accuracy, precision, recall, F1-score, ROC-AUC, etc.) to evaluate model performance.
• Ability to perform hyperparameter tuning and model optimization techniques to improve model accuracy and efficiency.
• Familiarity with cross-validation techniques to ensure model generalization.
• Knowledge of deployment methods for machine learning models, including cloud platforms (e.g., AWS, Azure, Google Cloud) and containerized environments (e.g., Docker, Kubernetes).
• Ability to monitor model performance in production, handling real-time data input, and troubleshooting when necessary.
• Experience in using APIs for model integration into applications or services.

Who should take the Exam?

This exam is highly recommended for professionals involved in the field of data science, machine learning, and artificial intelligence. It is especially suitable for individuals who wish to demonstrate their capabilities in building effective, scalable machine learning models from start to finish. The exam targets:

• Data Scientists and Machine Learning Engineers
• Software Engineers and Developers
• Aspiring Machine Learning Practitioners
• Beginners or those with a foundational knowledge of machine learning, who are looking to further enhance their skills in building and deploying models.

Course Outline

The Build Machine Learning Models Exam covers the following topics - 

Domain 1 - Introduction

• Overview of the Course
• What You Will Learn from This Course
• Expected Outcomes
• Structure of the Course
• Understanding Algorithms in Programming

Domain 2 - Data Preparation

• Importing Data from CSV Files
• Data Scaling: Normalization
• Data Scaling: Standardization
• Evaluation Methods for Algorithms
• Train-Test Split Explained
• Defining K-Fold Cross-Validation
• Implementing K-Fold Cross-Validation
• Choosing the Right Resampling Method
• Key Evaluation Metrics:
• Classification Accuracy
• Confusion Matrix
• Regression Metrics
• Baseline Models
• Random Prediction Algorithm
• Zero Rule Algorithm

Domain 3 - Linear Algorithms

• Algorithm Test Harness: Train-Test Split
• Algorithm Test Harness: K-Fold
• Introduction to Simple Linear Regression
• Simple Linear Regression Case Study: Part 1
• Simple Linear Regression Case Study: Part 2
• Multivariate Linear Regression Case Study
• Demo: Multivariate Linear Regression Case Study
• Demo: Linear Regression with Wine Quality Dataset
• Understanding Logistic Regression
• Demo: Logistic Regression for Predictions
• Demo: Estimating Coefficients with Logistic Regression
• Demo: Logistic Regression Applied to Diabetes Dataset
• Perceptron Overview
• Demo: Perceptron for Predictions
• Demo: Perceptron for Training Weights
• Demo: Perceptron with Sonar Dataset

Domain 4 - Non-Linear Regression

• Classification and Regression Trees (CART)
• Demo: CART and the Gini Index
• Demo: CART: Creating Splits
• Demo: CART: Evaluating Splits
• Building a Decision Tree with CART
• Demo: Recursive Splitting in CART
• Demo: Assembling the CART Tree
• Demo: Applying CART to Banknote Dataset
• Introduction to Naïve Bayes
• Demo: Naïve Bayes: Separation by Class
• Demo: Naïve Bayes: Dataset Summarization
• Demo: Naïve Bayes: Summarizing Data by Class