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Diploma in Practical Machine Learning with Tensor Flow
Learn about the fundamentals of machine learning and the application of TensorFlow in this free online course.
Description
This free online course in practical machine learning with TensorFlow will begin by introducing you to the concept of machine learning and the overview of TensorFlow. You will learn about the steps in the machine learning process, logistic regression and the loss unction in machine learning. You will also be introduced to gradient descent, gradient descent variations, machine learning visualization as well as confusion matrix.
The course then introduces the concept of tensors and their relevance. You will learn about the mathematical fundamentals of deep learning. You will also learn how to build data pipelines for TensorFlow as well as text processing with TensorFlow. Next, you will be introduced to machine learning models, text classification, overfitting, underfitting, regression and the architecture of neural network model.
The course then explains the meaning of convolution neural network and transfer learning. You will also learn about the pooling, and image classification and visualization. This course explains in great detail estimator API, boosted trees as well as word embeddings and its application. This course also explains the customization of TensorFlow, writing a custom layer as well as Tensorflow distributed training.
Start Course NowModules
Introduction to Tensorflow
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Introduction to Tensorflow - Learning Outcomes
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Overview of Tensorflow
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Machine Learning Refresher
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Steps in Machine Learning Process
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Loss Function in Machine Learning
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Gradient Descent
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Introduction to Tensorflow - Lesson Summary
Machine Learning Concepts
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Machine Learning Concepts - Learning Outcomes
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Gradient Descent Variations
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Model Selection and Evaluations
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Machine Learning Visualization
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Deep Learning Refresher
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Introduction to Tensor
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Machine Learning Concepts - Lesson Summary
Data Pipelines and Text Processing
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Data Pipelines and Text Processing - Learning Outcomes
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Mathematical Fundamental of Deep Learning
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Building Data Pipelines for Tensorflow I
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Building Data Pipelines for Tensorflow II
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Building Data Pipelines for Tensorflow III
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Text Processing with Tensorflow
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Data Pipelines and Text Processing - Lesson Summary
Building Machine Learning Models
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Building Machine Learning Models - Learning Outcomes
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Classify Image
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Regression
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Classify Structured Data
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Text Classification
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Underfitting and Overfitting
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Save and Restore Models
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Building Machine Learning Models - Lesson Summary
Diploma in Practical Machine Learning with Tensor Flow - First Assessment
Convolutional Neural Network
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Convolutional Neural Network - Learning Outcomes
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Convolutional Neural Networks
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Key Operations in CNNs
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Transfer Learning with Pre-trained CNNs
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Transfer Learning with Tensorflow Hub
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Image Classification and Visualization
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Convolutional Neural Network - Lesson Summary
Training and Embedding
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Training and Embedding - Learning Outcomes
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Estimator API
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Logistic Regression
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Boosted Trees
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Introduction to Word Embeddings
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Convolutional Neural Network - Lesson Summary
Introduction to Recurrent Neural Networks
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Introduction to Recurrent Neural Networks - Learning Outcomes
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Recurrent Neural Networks
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Recurrent Neural Networks II
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Time Series Forecasting with RNNs
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Text Generation with RNNs
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Introduction to Recurrent Neural Networks - Lesson Summary
Training and Customizing
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Training and Customizing - Learning Outcomes
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Tensorflow Customization
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Customizing Tensorflow Keras
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Tensorflow Keras Concepts
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Tensorflow Distributed Training
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Training and Customizing - Lesson Summary
Diploma in Practical Machine Learning with Tensor Flow - Second Assessment
Course assessment
Learning Outcomes
Upon successful completion of this course, you will be able to:
- Discuss the history and development of Tensorflow
- Distinguish between machine learning and traditional programming
- Explain the importance of a neural network playground
- Analyze the mathematical foundations of deep learning
- Explain the process of using TensorFlow API to build a deep learning model for regression problems
- Discuss image classification and visualization
- Explain the methods of improving model accuracy
Certification
All Alison courses are free to enrol, study and complete. To successfully complete this Diploma course and become an Alison Graduate, you need to achieve 80% or higher in each course assessment. Once you have completed this Diploma course, you have the option to acquire an official Diploma, which is a great way to share your achievement with the world. Your Alison Diploma is:
Ideal for sharing with potential employers - include it in your CV, professional social media profiles and job applications
An indication of your commitment to continuously learn, upskill and achieve high results
An incentive for you to continue empowering yourself through lifelong learning
Alison offers 3 types of Diplomas for completed Diploma courses:
Digital Diploma - a downloadable Diploma in PDF format, immediately available to you when you complete your purchase
Diploma - a physical version of your officially branded and security-marked Diploma, posted to you with FREE shipping
Framed Diploma - a physical version of your officially branded and security-marked Diploma in a stylish frame, posted to you with FREE shipping
All Diplomas are available to purchase through the Alison Shop. For more information on purchasing Alison Diplomas, please visit our FAQs. If you decide not to purchase your Alison Diploma, you can still demonstrate your achievement by sharing your Learner Record or Learner Achievement Verification, both of which are accessible from your Dashboard. For more details on our Diploma pricing, please visit our Pricing Page.
