This Professional Certificate consists of 6 self-paced courses. Effort required to complete each course is 4-5 weeks if spending 2-4 hours per week. At this rate the entire specialization can be completed in 3-6 months.
This Professional Certificate pre-requisties the following skills:
- Working Knowledge of Python Programming language and Jupyter Notebooks e.g. Python for Data Science and AI
- High School Mathematics or Math for Machine Learning
It is highly recommended that you complete either or both of the following Professional Certificates before starting this one:
SKILLS YOU WILL GAIN
Data ScienceDeep LearningArtificial Intelligence (AI)Machine LearningApache Spark
Upon completing this Professional Certificate you will be able to:
- Describe what is Machine Learning (ML), Deep Learning (DL) & Neural Networks
- Explain ML algorithms including Classification, Regression, Clustering, and Dimensional Reduction
- Implement Supervised and Unsupervised ML models using scipy and scikitlearn
- Express how Apache Spark works and how to perform Machine Learning on Big Data
- Deploy ML Algorithms and Pipelines on Apache Spark
- Demonstrate an understanding of Deep Learning models such as autoencoders, restricted Boltzmann machines, convolutional networks, recursive neural networks, and recurrent networks
- Build deep learning models and neural networks using the Keras library
- Utilize the PyTorch library for Deep Learning applications and build Deep Neural Networks
- Explain foundational TensorFlow concepts like main functions, operations & execution pipelines
- Apply deep learning using TensorFlow and perform backpropagation to tune the weights and biases
- Determine what kind of deep learning method to use in which situation and build a deep learning model to solve a real problem
- Demonstrate ability to present and communicate outcomes of deep learning projects
To apply: