[Udemy] Machine Learning With TensorFlow: The Practical Guide
What you’ll learn
- Learn core concepts of Tensorflow
- Learn to implement machine learning algorithms in Tensorflow
- Start building your own apps and solutions in Tensorflow
- Learn concepts such as Supervised learning, Unsupervised learning and neural networks
- Basic knowledge of any programming language is sufficient to start this course
Machine learning has become one of the most common practices used by many organizations, groups and individuals. It helps various software to predict the outcome more precisely without any programming. Machine learning finds the pattern in the input data and uses statistical analysis to foretell the result. To support its extensive requirements, Tensorflow was launched by Google. In order to provide next-generation machine learning solutions, we have hand-picked this course covering all its aspects.
Why this course is important?
Machine learning often requires heavy computation and for that Tensorflow was developed as an open source library. Tensorflow not only does the heavy computation but can also build dataflows. Apart from machine learning, it is also used in wide variety of other domains by the experts. This course contains different topics to make you understand everything about next-generation machine learning by Tensorflow.
What makes this course so valuable?
It includes all the basics of Tensorflow with detail description of tensors, operators and variables. Installation of Tensorflow on Windows, Mac and Linux is clearly shown. Additionally, it gives insights into the basics of machine learning and its types. This course also covers various algorithms like linear regression, logistic regression, NN regression, K-Means algorithm and others. Herein, advanced machine learning is also well elaborated with the topics of neural networks, convolution neural networks, recurrent neural networks and so on.
This course includes-
1.Tensorflow fundamentals and installation
2. Details about tensors, operators, variables and others
3. Details about machine learning, inference and its types
4. Different algorithms like linear regression, logistic regression, clustering, K-means algorithm, kernels and many more
5. Various advanced learning networks and its implementation – Neural Networks, Convolution Neural Network, Recurrent Neural Networks
6. At the end of each section, a quiz is also provided to check how well you have grasped all the topics
7. Finally, a project on Deep Neural Networks using Tensorflow is given to ensure its correct implementation.
As they say, it’s never too late to start something new. So, stop thinking and start now with next-generation machine learning with Tensorflow.
Who this course is for:
- Anyone who wants to master Tensorflow and machine learning will find this course very useful