This 4-course specialization by deeplearning.ai I learned how to conceptualize and maintain integrated Machine Learning systems. I mastered well-established tools and methodologies to build production systems that can handle relentless evolving data and continuously run at maximum efficiency. I’m now familiar with the capabilities, challenges, and consequences of machine learning engineering in production. I learned how to:

1. Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
2. Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
3. Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
4. Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.

It consists of the following courses:

1. Introduction to Machine Learning in Production
2. Machine Learning Data Lifecycle in Production
3. Machine Learning Modeling Pipelines in Production
4. Deploying Machine Learning Models in Production

This 5-course specialization by deeplearning.ai was created and is taught by Dr. Andrew Ng, a global leader in AI and co-founder of Coursera. It’s designed to prepare learners to participate in the development of cutting-edge AI technology, and to understand the capability, the challenges, and the consequences of the raise of deep learning. It helped me learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. I learned about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. I worked on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. I practiced all these ideas in Python and in TensorFlow.

It consists of the following courses:

1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks - Hyperparameter Tuning, Regularization and Optimization
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models