Fundamentals of AI & ML: Advanced Data Science Methods

Artificial Intelligence 2023    |    Intermediate
  • 14 videos | 1h 9m 24s
  • Includes Assessment
  • Earns a Badge
  • Certification PMI PDU
Rating 4.4 of 136 users Rating 4.4 of 136 users (136)
In data science, many statistical and analytical techniques can be used to pull meaningful insights from data. Some advanced data science methods rely on other foundational data science methods, such as text mining. In this course, you will learn about advanced data science methods and their use cases. Begin this course with an exploration of advanced machine learning (ML) methods, such as text mining and graph analysis, and their uses. Next, you will discover the anomaly and novelty detection processes. You will examine association rule mining and neural networks, including their use cases across industries. Then you will focus on common challenges during artificial intelligence (AI) and ML model training, the trade-offs between model complexity and interpretability, and the role of natural language processing (NLP) in text analysis. Finally, you will investigate the potential of computer vision techniques and applications of reinforcement learning.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Identify the concept and use cases for text mining
    Outline strategies for evaluating the accuracy of text mining, as well as common text mining pitfalls
    State the purpose of and use cases for graph analysis
    Recognize details for anomaly detection and its use cases
    Describe the process of and use cases for novelty detection
    Outline rule mining and its use cases
  • Identify the concept and use cases for neural networks
    Identify common challenges faced during training and optimization of ai and machine learning (ml) models
    Analyze the trade-offs between model complexity and interpretability
    Describe the role of nlp in text analysis and language understanding and identify nlp use cases
    Outline the potential of computer vision techniques in image recognition and object detection
    Define reinforcement learning, including its application in dynamic decision-making scenarios
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 44s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 3m 32s
    Upon completion of this video, you will be able to identify the concept and use cases for text mining. FREE ACCESS
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    3.  Evaluate Text Mining Accuracy
    1m 59s
    After completing this video, you will be able to outline strategies for evaluating the accuracy of text mining as well as common text mining pitfalls FREE ACCESS
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    4.  Graph Analysis
    5m 37s
    In this video, we will state the purpose of and use cases for graph analysis. FREE ACCESS
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    5.  Anomaly Detection
    7m 18s
    Upon completion of this video, you will be able to recognize details for anomaly detection and its use cases. FREE ACCESS
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    6.  Novelty Detection
    2m 57s
    After completing this video, you will be able to name the process and use cases for novelty detection. FREE ACCESS
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    7.  Association Rule Mining
    4m 40s
    In this video, we will outline rule mining and its use cases. FREE ACCESS
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    8.  Neural Networks
    7m 53s
    After completing this video, you will be able to identify the concept and use cases for neural networks. FREE ACCESS
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    9.  AI Model Training and Optimization
    6m 17s
    Upon completion of this video, you will be able to identify common challenges faced during training and optimization of AI and machine learning (ML) models. FREE ACCESS
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    10.  Model Complexity and Interpretability
    4m 44s
    In this video, we will analyze the trade-offs between model complexity and interpretability. FREE ACCESS
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    11.  Natural Language Processing (NLP) in Text Analysis
    7m 37s
    After completing this video, you will be able to describe the role of NLP in text analysis and language understanding and identify NLP use cases. FREE ACCESS
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    12.  Computer Vision in Image Recognition and Object Detection
    8m 11s
    Upon completion of this video, you will be able to outline the potential of computer vision techniques in image recognition and object detection. FREE ACCESS
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    13.  Reinforcement Learning
    7m 29s
    After completing this video, you will be able to define reinforcement learning, including its application in dynamic decision-making scenarios. FREE ACCESS
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    14.  Course Summary
    27s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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