Keras for Machine Learning Practice Exam
Keras for Machine Learning Practice Exam
About Keras for Machine Learning Exam
The Keras for Machine Learning exam is designed to assess your knowledge and expertise in using Keras, a popular deep learning library built on top of TensorFlow, for building and deploying machine learning models. This exam covers essential concepts related to neural networks, deep learning architectures, and the application of Keras for model building, evaluation, and optimization. It tests your ability to understand and implement various machine learning algorithms using Keras, such as classification, regression, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. Additionally, the exam evaluates your skills in using Keras for data preprocessing, model evaluation, and hyperparameter tuning.
Skills Required
- Proficiency in Python programming, including basic libraries like NumPy and Pandas.
- Understanding of machine learning fundamentals, including supervised and unsupervised learning techniques.
- Familiarity with neural networks, deep learning models, and their architectures.
- Experience with Keras for model development, including creating, training, and evaluating neural networks.
- Knowledge of Keras API, including layers, optimizers, activation functions, and loss functions.
- Experience in using Keras for real-world machine learning applications such as image and text classification, time series forecasting, and more.
- Knowledge of model optimization techniques, including regularization and hyperparameter tuning.
- Understanding of the integration of Keras with TensorFlow for model deployment.
Who should take the Exam?
This exam is intended for machine learning practitioners, data scientists, AI engineers, and software developers who wish to demonstrate their proficiency in Keras for building deep learning models. It is ideal for individuals who have a solid understanding of machine learning concepts and are looking to expand their skills in implementing deep learning models using Keras. Whether you are a beginner in deep learning or an experienced developer looking to validate your expertise, this exam will help you assess your capabilities in using Keras to build and optimize machine learning models.
Course Outline
The Keras for Machine Learning Exam covers the following topics -
Domain 1 - Overview
- Introduction to Keras
Domain 2 - Core Concepts
- Theano Overview
- Understanding TensorFlow
- Anatomy of an Artificial Neural Network
- Introduction to Deep Learning
- Understanding the Keras Workflow
- Breakdown of Keras Code Structure
- Demonstration: Pima Indian Diabetes Dataset – Data Loading
- Demonstration: Pima Indian Diabetes Dataset – Model Definition and Compilation
- Demonstration: Pima Indian Diabetes Dataset – Model Fitting and Evaluation
- Neural Network Performance Evaluation
- Demonstration: Data Segmentation Case Study
- Utilizing Scikit-Learn for General Machine Learning
- Evaluating Models Using Cross-Validation
- Hyperparameter Tuning with Grid Search in Deep Learning
- Demonstration: Multiclass Classification Case Study
- Demonstration: Multiclass Classification – Part 2
- Demonstration: Binary Classification Case Study
- Demonstration: Binary Classification – Part 2
- Demonstration: Binary Classification – Part 3
- Demonstration: Binary Classification – Part 4
- Demonstration: Regression Case Study
- Demonstration: Regression – Part 2
- Demonstration: Regression – Part 3
Domain 3 - Advanced Techniques in Keras
- Model Serialization Process
- Saving Neural Networks in JSON Format
- Saving Neural Networks in YAML Format
- Demonstration: Checkpointing Case Study
- Demonstration: Checkpointing – Part 2
- Plotting and Visualizing Training History
- Demonstration: Visualizing Model Training History in Keras
- Demonstration: Implementing Dropout in Models
- Demonstration: Dropout – Part 2
- Tips for Efficient Dropout
- Defining Learning Rates
- Configuring Learning Rate Adjustments
- Demonstration: Learning Rate Case Study
- Demonstration: Learning Rates – Part 2
- Demonstration: Learning Rates – Part 3
Domain 4 - Convolutional Neural Networks (CNNs)
- Introduction to Convolutional Neural Networks
- Demonstration: Handwritten Digit Recognition Case Study
- Demonstration: Handwritten Digit Recognition – Part 2
- Demonstration: Handwritten Digit Recognition – Part 3
- Demonstration: Handwritten Digit Recognition – Part 4
- Implementing Image Augmentation
- Demonstration: Image Augmentation Case Study
- Demonstration: Image Augmentation – Part 2
- Tips for Effective Image Augmentation
- Object Recognition in Keras
- Demonstration: Object Recognition Case Study
- Techniques for Enhancing Model Performance
- Sentiment Analysis with Keras
- Understanding IMDB Dataset Features
- Overview of Word Embedding Concepts
- Demonstration: Word Embedding Case Study
- Demonstration: Word Embedding – Part 2
Domain 5 - Recurrent Neural Networks (RNNs)
- Introduction to Recurrent Neural Networks
- Demonstration: Time Series Prediction Case Study
- Demonstration: Time Series Prediction – Part 2
- Demonstration: Time Series Prediction – Part 3
- Demonstration: Time Series Prediction with LSTM Networks
- Demonstration: Time Series Prediction with LSTM – Part 2
- Demonstration: Time Series Prediction with LSTM – Part 3
- Demonstration: Sequence Classification Case Study
- Demonstration: Sequence Classification – Part 2
