C1000-142

The main points of the IBM C1000-142 exam are:

• Data Science Foundations . This section covers the foundational concepts of data science, including data collection, data preparation, and data modeling.
• Machine Learning . This section covers the core concepts of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
• Deep Learning . This section covers the core concepts of deep learning, including artificial neural networks, convolutional neural networks, and recurrent neural networks.
• Natural Language Processing . This section covers the core concepts of natural language processing, including text classification, text summarization, and question answering.
• Computer Vision . This section covers the core concepts of computer vision, including object detection, image segmentation, and scene understanding.
• Data Engineering . This section covers the core concepts of data engineering, including data warehousing, data lakes, and big data processing.

In addition to these core concepts, the IBM C1000-142 exam also covers a variety of specific topics related to data science, machine learning, deep learning, natural language processing, and computer vision. These topics include:

• Data mining
• Statistical modeling
• Predictive analytics
• Data visualization
• Big data
• Cloud computing
• Artificial intelligence

By passing the IBM C1000-142 exam, you will demonstrate your ability to apply data science techniques to solve real-world problems. You will also be able to show that you have the skills and knowledge necessary to work as a data scientist in a variety of industries.