Online Course – Certified Professional Internship in Machine Learning with Google Cloud’s TensorFlow
Learn about machine learning (ML) in the Google Cloud. Hands-on courses with real-world data to experiment with include exercises and in-depth information.
Problems that can be solved with machine learning.
The five steps to convert use cases
Step 1: Defining the problem.
Step 2: Collect data.
Step 3: Data processing.
Step 4: Building the model.
Step 5: Evaluate the model.
The importance of the steps
Why is it important not to skip these steps?
Focusing on neural networks
Understanding the reasons for focusing on neural networks today.
Establishing a problem and finding a solution
Establishing a problem.
Finding a suitable solution using gradient descent.
Creating a data set.
Building distributed models
Using Tensorflow.
Expanding model training.
Achieving highly actionable forecasts.
Machine Learning (ML)
How ML learns important features from data.
Integrating human analysis into problems.
Creating accurate and comprehensive models
Understanding the theory of solving certain ML problems.
Combining the appropriate parameters.
Building an ML-focused strategy
Practice the training process.
Optimization and full launch of models.
The manual work lab on Google Cloud Platform.
Hands-on Learning Project
Work labs that integrate the Qwiklabs platform.
Using the skills learned in the lecture videos.
Topics like Google Cloud Platform products.
Practical experiences
Practical experiences of the terms discussed in all modules.
Details of the courses that make up the specialization
How Google does machine learning
Course 1
Duration: 7 hours
Rating: 4.5 (132 ratings)
Course Details
What you’ll learn:
An explanation of the Vertex AI platform, and how to build, train, and launch machine learning models in AutoML without the need to write code.
Explaining best practices for implementing machine learning in Google’s cloud.
Utilizing Google Cloud Platform tools and environments for machine learning purposes.
Explanation of best practices for problem islands and responsible islands.
Skills you will gain
Category: Retake Exam
Category: Uncertainty Analysis
Category: Financial Analysis
Category: Motion diagram for launching into the world of machine learning
Course 2
Duration: 15 hours
Rating: 4.4 (50 ratings)
Course Details
What you’ll learn:
An explanation of how to improve data quality and how to perform exploratory data analysis.
Building and training AutoML models using Vertex AI and BigQuery ML.
Optimization and evaluation of models using loss functions and performance metrics.
Creating data systems for training, evaluation, and testing in a way that can be replicated and expanded.
Familiarity with TensorFlow.
Course 3
Duration: 19 hours
Rating: 3.8 (12 ratings)
Course Details
What you’ll learn:
The goal of this course is to create, train, and launch flexible and robust machine learning models using TensorFlow 2.x and Keras.
Learn about the TensorFlow 2.x API hierarchy and understand the key components of TensorFlow through hands-on exercises.
Become familiar with the methods for working with datasets and the costs of data entry in the TensorFlow 2.x process.
Perform hands-on exercises with tf.data.Dataset to load csv data, NumPy arrays, text data, and images.
Practices for preparing numerical, categorical, category, and smallness properties.
Learn how to create machine learning models using the Keras Sequential API and the Keras Functional API.
Understand about activation, loss, and optimization functions.
As part of the Jupyter Notebook exercises, build basic linear regression, basic logistic regression, and advanced logistic regression models.
Learn how to train, launch, and run machine learning models at scale on Cloud AI Platform.
Course 4
Duration: 9 hours
Rating: 4.5 (10 ratings)
Course Details
What you’ll learn:
Explain the Vertex AI feature store and compare the key aspects required for good features.
Perform feature engineering using BigQuery ML, Keras, and TensorFlow.
Discuss using Dataflow and Dataprep for feature preparation and exploration.
Use tf.Transform.
Skills you will gain
Category: Language Industry
Category: Information Seeking Behavior
Category: Collective Intelligence
Category: Social Media Mining
Course 5
Duration: 18 hours
Rating: 4.4 (10 ratings)
Course Details
What you’ll learn:
Welcome to the course “The Art and Science of Machine Learning.” The course includes 6 modules.
The course explains the knowledge, sound judgment, and basic skills required to accurately optimize machine learning models to achieve optimal performance.
Learn how to use regularization techniques to generalize models and also understand the impact of hyperparameters (such as the impact of array size or learning rate on model performance).
Explain some classic optimization algorithms and describe how to specify optimization methods in TensorFlow code.
