Human-Level Performance is the error a person may get. It's usually based on what your objective is. 
On the opposite to that is the Bayes (optimal) error which is the theoretical error. And this error may not be 0. Since there might be some impossible cases to classify, e.g., a picture that is too blurry to be recognized.
Before surpassing human-level performance, the accuracy will be improved greatly. However, when it passes the line, it will get harder since you cannot learn from human, and you don't know what the true Bayes error is.

Human Level Performance

https://www.coursera.org/learn/machine-learning-projects?specialization=deep-learning

University of Michigan - Ann Arbor

Coursera's five-course Deep Learning Specialization covers neural networks, hyperparameter tuning, regularization, optimization, machine-learning project strategy, convolutional neural networks, and sequence models. The courses are Neural Networks and Deep Learning; Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization; Structuring Machine Learning Projects; Convolutional Neural Networks; and Sequence Models. https://www.coursera.org/specializations/deep-learning

Coursera: Deep Learning Specialization

https://www.coursera.org/learn/neural-networks-deep-learning?specialization=deep-learning

Neural Networks and Deep Learning

https://www.coursera.org/learn/deep-neural-network?specialization=deep-learning

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Structuring Machine Learning Projects

https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning

Convolutional Neural Networks

https://www.coursera.org/learn/nlp-sequence-models

Sequence Models

https://www.coursera.org/professional-certificates/ibm-data-science
Beginner course

IBM Data Science Professional Certificate

https://www.coursera.org/specializations/jhu-data-science
ten course specialization on coursera offered by JHU staff

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