High Success Rate
Globally Renowned Trainer
Real-time code analysis and feedback
This course teaches participants the following skills:
- Frame a business use case as a machine learning problem
- Create machine learning datasets that are capable of achieving generalization
- Implement machine learning models using TensorFlow
- Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalization
- Build and operationalize distributed TensorFlow models
- Represent and transform features
How Google Does Machine Learning
- What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently: it’s about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important not to skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them. Course Objectives:
Develop a data strategy around machine learning
Examine use cases that are then reimagined through an ML lens
Recognize biases that ML can amplify
Leverage Google Cloud Platform tools and environment to do ML
Learn from Google’s experience to avoid common pitfalls
Carry out data science tasks in online collaborative notebooks
Invoke pre-trained ML models from Cloud Datalab
Launching into Machine Learning
- Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation. Course Objectives:
Identify why deep learning is currently popular
Optimize and evaluate models using loss functions and performance metrics
Mitigate common problems that arise in machine learning
Create repeatable and scalable training, evaluation, and test datasets
Intro to TensorFlow
- We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Course Objectives:
Create machine learning models in TensorFlow
Use the TensorFlow libraries to solve numerical problems
Troubleshoot and debug common TensorFlow code pitfalls
Use tf.keras and tf_estimator to create, train, and evaluate ML models
Train, deploy, and productionalize ML models at scale with Cloud ML Engine
- A key component of building effective machine learning models is to convert raw data to features in a way that allows ML to learn important characteristics from the data. We discuss how to represent features and code this up in TensorFlow. Human insight can be brought to bear in machine learning problems through the use of custom feature transformations. In this module, we talk about common types of transformations and how to implement them at scale.
Turn raw data into feature vectors
Preprocess and create new feature pipelines with Cloud Dataflow
Create and implement feature crosses and assess their impact
Write TensorFlow Transform code for feature engineering
The Art and Science of ML
- Machine Learning is both an art that involves knowledge of the right mix of parameters that yields accurate, generalized models and a science that involves knowledge of the theory to solve specific types of ML problems. We discuss regularization, dealing with sparsity, multi-class neural networks, reusable embeddings, and many other essential concepts and principles.
Optimize model performance with hyperparameter tuning
Experiment with neural networks and fine-tune performance
Enhance ML model features with embedding layers
Create reusable custom model code with the Custom Estimator
To get the most out of this specialization, participants should have:
- Experience coding in Python
- Knowledge of basic statistics
- Knowledge of SQL and cloud computing
Join Machine Learning with TensorFlow on Google Cloud Platform and gain the knowledge and skills needed to land an entry-level job in Machine Learning. A Path to In-Demand Jobs.
When you complete the course in the program, you’ll earn a Certificate to share with your professional network as well as unlock access to career support resources to help you kickstart your new career. Many Professional Certificates have hiring partners that recognize the Professional Certificate credential and others can help prepare you for a certification exam. You can find more information on individual Professional Certificate pages where it applies.
- High Success rate
- Join Our Dynamic Community
- Training from Recognized Trainer
- Post-workshop support by the Coaches
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