Machine Learning With TensorFlow: The Practical Guide
Machine Learning With TensorFlow: The Practical Guide, available at $19.99, has an average rating of 3.95, with 41 lectures, 3 quizzes, based on 16 reviews, and has 666 subscribers.
You will learn about 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 This course is ideal for individuals who are Anyone who wants to master Tensorflow and machine learning will find this course very useful It is particularly useful for Anyone who wants to master Tensorflow and machine learning will find this course very useful.
Enroll now: Machine Learning With TensorFlow: The Practical Guide
Summary
Title: Machine Learning With TensorFlow: The Practical Guide
Price: $19.99
Average Rating: 3.95
Number of Lectures: 41
Number of Quizzes: 3
Number of Published Lectures: 41
Number of Published Quizzes: 3
Number of Curriculum Items: 44
Number of Published Curriculum Objects: 44
Original Price: $49.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Anyone who wants to master Tensorflow and machine learning will find this course very useful
Target Audiences
- Anyone who wants to master Tensorflow and machine learning will find this course very useful
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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Chapter 2: Getting started with Tensorflow
Lecture 1: Introduction
Lecture 2: Installing Tensorflow on Windows
Lecture 3: Installing Tensorflow on Mac
Lecture 4: Installing Tensorflow on Linux
Lecture 5: Tensorflow Fundamentals
Lecture 6: A Simple Example Using Tensorflow
Chapter 3: Tensorflow Basics
Lecture 1: Tensors
Lecture 2: Operators
Lecture 3: Operators with Sessions
Lecture 4: Variables
Lecture 5: Using Jupyter
Lecture 6: Using Tensorboard
Chapter 4: Machine Learning Basics
Lecture 1: What is Machine Learning
Lecture 2: Learning and Inference
Lecture 3: Types of Machine Learning – Supervised, Unsupervised, Reinforcement
Chapter 5: Main Algorithms
Lecture 1: Linear Regression
Lecture 2: Linear Regression Implementation using Tensorflow
Lecture 3: Logistic Regression
Lecture 4: Logistic Regression Implementation using Tensorflow
Lecture 5: NN Regression
Lecture 6: K Nearest Neighbours – Classification
Lecture 7: K Nearest Neighbours – Pseudocode and Error Rate
Lecture 8: KNN Implementation using Tensorflow
Lecture 9: What is Clustering?
Lecture 10: Clustering Approaches
Lecture 11: K-Means Algorithm
Lecture 12: K-Means Implementation using Tensorflow
Lecture 13: Support Vector Machines (SVM)
Lecture 14: Kernels – Introduction
Lecture 15: Kernels
Lecture 16: SVM Implementation using Tensorflow
Chapter 6: Advance ML
Lecture 1: Neural Networks – Introduction
Lecture 2: Neural Networks – Feedforward, Backpropogation, Error
Lecture 3: Neural Networks Implementation using Tensorflow
Lecture 4: Convolutional Neural Networks (CNN)
Lecture 5: CNN Implementation using Tensorflow
Lecture 6: Recurrent Neural Networks (RNN)
Lecture 7: RNN Implementation using Tensorflow
Lecture 8: A Project on Deep Neural Networks using Tensorflow
Lecture 9: Bonus Lecture: More Interesting Stuff, Offers and Discounts
Instructors
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Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses -
Eduonix-Tech .
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- 3 stars: 7 votes
- 4 stars: 5 votes
- 5 stars: 4 votes
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