Machine Learning With TensorFlow The Practical Guide
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Machine Learning With TensorFlow The Practical Guide at Eduonix Overview
Duration | 16 hours |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Credential | Certificate |
Machine Learning With TensorFlow The Practical Guide at Eduonix Highlights
- Skills Covered: Hyperparameter, Tensorflow, Hyperparameter Optimization, Deep Learning
- Lifetime Access. No Limits!
- Learn machine learning with real-world scenarios
- Self paced Course
Machine Learning With TensorFlow The Practical Guide at Eduonix Course details
- Software developers and programmers, data scientists, data analysts, robotics professionals, and computation and educational professionals, among others.
- Create your own machine learning algorithms
- Leverage the real data acquired by real companies
- Optimize business performance, retain customers, improve conversions
- This course gives an insight into the basics of Tensorflow covering topics like tensors, operators and variables. It is a good option to master machine learning, its types and various main algorithms including linear regression. Furthermore, this course also covers advanced machine learning like a neural network, convolution neural network and others. Here, you'll also gain the practice by implementing it in a project on Deep Neural Network.
- Machine Learning With TensorFlow is a professional programme. It provides a complete insight of Tensorflow, tensors, operators and variables. Machine Learning With TensorFlow also includes other topics like Getting started with Tensorflow, Tensorflow Basics, Machine Learning Basics, Main Algorithms including linear regression, Advanced Machine Learning
Machine Learning With TensorFlow The Practical Guide at Eduonix Curriculum
Section 1 : Introduction
Introduction
Section 2 : Getting started with Tensorflow
Introduction
Installing Tensorflow on Windows
Installing Tensorflow on Mac
Installing Tensorflow on Linux
Tensorflow Fundamentals
A Simple Example Using Tensorflow
Section 3 : Tensorflow Basics
Tensors
Operators
Operators with Sessions
Variables
Using Jupyter
Using Tensorboard
Quiz: Section 3 Quiz
Section 4 : Machine Learning Basics
What is Machine Learning
Learning and Inference
Types of Machine Learning - Supervised, Unsupervised, Reinforcement
Quiz: Section 4 Quiz
Section 5 : Main Algorithms
Linear Regression
Linear Regression Implementation using Tensorflow
Logistic Regression
Logistic Regression Implementation using Tensorflow
NN Regression
K Nearest Neighbours Classification
K Nearest Neighbours - Pseudocode and Error Rate
KNN Implementation using Tensorflow
What is Clustering?
Clustering Approaches
K-Means Algorithm
K-Means Implementation using Tensorflow
Support Vector Machines (SVM)
Kernels Introduction
Kernels
SVM Implementation using Tensorflow
Section 6 : Advance ML
Neural Networks Introduction
Neural Networks - Feedforward, Backpropogation, Error
Neural Networks Implementation using Tensorflow
Convolutional Neural Networks (CNN)
CNN Implementation using Tensorflow
Recurrent Neural Networks (RNN)
RNN Implementation using Tensorflow
A Project on Deep Neural Networks using Tensorflow
Quiz: Section 6 Quiz
Machine Learning With TensorFlow The Practical Guide at Eduonix Entry Requirements
- Educational Qualifications The applicant must have a Bachelor's Degree or an equivalent qualification in any discipline.
- Work Experience: The applicant should have a basic knowledge of any programming language
- Not mentioned
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