Python & TensorFlow Masterclass

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About Course

Develop a clear understanding of the tools and techniques used for machine learning and AI with our Python & TensorFlow Masterclass. This online training is dedicated to providing quality training on TensorFlow helping you reach for a promising career in the AI industry.

The comprehensive course will help you develop a clear understanding of machine learning. Here, you will receive detailed lessons on TensorFlow basics. The course will help you grasp the concepts of supervised and unsupervised learning. Then you will learn about neural networks and model optimisation. Finally, you will grasp the skills required to use TensorFlow for production.

At the end of the course, you will receive a certificate of achievement. This certificate will enhance your employability in the IT industry. Join now!

Certificate Of Achievement

After completing this course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates & Transcripts can be obtained either in Hardcopy at USD19.30 or in PDF format at USD15.44.

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What Will You Learn?

  • TensorFlow Installation
  • Neural Netwroks
  • Model Evaluation and Optimisation
  • Get an overall understanding of machine learning
  • Understand the basics and the installation process of TensorFlow
  • Receive detailed lessons on supervised learning and unsupervised learning
  • Enhance your knowledge and understanding of neural networks
  • Learn the fundamentals of the model evaluation and optimisation
  • Know the skills for using TensorFlow for production

Course Content

What is TensorFlow?

  • What is TensorFlow?
    00:00

What is Supervised Learning?

What is Unsupervised Learning?

What is Machine Learning?

What is Deep Learning?

What are Neural Networks?

Types of Machine Learning

TensorFlow APls

A refresher on APIs

Additional Materials – Python & TensorFlow: Deep Dive into Machine Learning

Applications of Machine Learning

Basic Neural Networks

Building Deep Neural Networks

Convolutional Neural Networks (CNNs)

Decision Trees

Deploying TensorFlow models

Distributed TensorFlow

Hierarchical Clustering

Hyperparameter Tuning

Installing and Setting up TensorFlow

K-Means Clustering

Linear Regression

Logistic Regression

ML Project: Image Classification Model

Model Evaluation Metrics

Overfitting and Underfitting

Principal Component Analysis (PCA)

Random Forests

Recurrent Neural Networks (RNNs)

Saving and restoring models

Support Vector Machines (SVMs)

TensorBoard for visualization and debugging

TensorFlow Architecture

Training and Testing Data

Conclusion

Certificates and Transcripts

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