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TensorFlow Training (11 Courses, 3+ Projects)
This TensorFlow Certification includes 11 Courses, 3 Projects with 55+ hours of video tutorials and Lifetime access.
You get to learn TensorFlow for Deep Learning with Python. We will help you learn to build a neural network and how to train, evaluate and optimize it with TensorFlow.
MOST POPULAR
1
Machine Learning with Tensorflow
2
Project on Tensorflow - Implementing Linear Model with Python
3
Hands-on Deep Learning Training
4
Deep Learning Tutorials
5
Pandas with Python Tutorial
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* One Time Payment & Get Lifetime Access
* One Time Payment & Get Lifetime Access
What you get in this TensorFlow Training?
About TensorFlow Course
| Course | No. of Hours | |
|---|---|---|
| Machine Learning with Tensorflow | 13h 29m | |
| Project on Tensorflow - Implementing Linear Model with Python | 1h 46m | |
| Hands-on Deep Learning Training | 10h 8m | |
| Deep Learning Tutorials | 1h 34m | |
| Pandas with Python Tutorial | 5h 47m | |
| NumPy and Pandas Python | 5h 01m | |
| Pandas Python Case Study - Data Management for Retail Dataset | 3h 28m | |
| Matplotlib for Python Developers - Beginners | 4h 12m | |
| Matplotlib for Python Developers - Intermediate | 2h 52m | |
| Matplotlib for Python Developers - Advanced | 6h 37m | |
| Seaborn Python - Beginners | 2h 28m | |
| Seaborn Python - Intermediate | 1h 18m | |
| Seaborn Python - Advanced | 1h 56m | |
| Case Studies on Seaborn Python Basics | 1h 51m |
| Course Name | Online TensorFlow Training Course |
| Deal | You get access to all 11 courses, 3 Projects bundle. You do not need to purchase each course separately. |
| Hours | 55+ Video Hours |
| Core Coverage | TensorFlow for Deep Learning with Python |
| Course Validity | Lifetime Access |
| Eligibility | Anyone serious about learning Machine Learning and wants to make a career in this Field |
| Pre-Requisites | Basic knowledge about machine learning would be preferable |
| What do you get? | Certificate of Completion for each of the 11 courses, 3 Projects |
| Certification Type | Course Completion Certificates |
| Verifiable Certificates? | Yes, you get verifiable certificates for each course with a unique link. These link can be included in your resume/Linkedin profile to showcase your enhanced skills |
| Type of Training | Video Course – Self Paced Learning |
| Software Required | None |
| System Requirement | 1 GB RAM or higher |
| Other Requirement | Speaker / Headphone |
TensorFlow Course Curriculum
In this section, each module of the TensorFlow Course is explained.
This TensorFlow Certification is from Edu-CBA Academy Courses which is a package of two online courses and many chapters with its topics included under each course. The TensorFlow Course and the relative chapters are also covered under each chapter with basics and advanced concepts on the latest TensorFlow library, tools and its several related frameworks that come under deep learning techniques and its applications. This TensorFlow training contains a total of 11 online courses including a course completion certification and also with lifetime access to all of its course contents.
To enable and provide the Edu CBA Academy learners with the greatest learning and development experience, this TensorFlow Course had been designed in a fine way that requires a minimum of 55+ hours to complete the courses under TensorFlow Certification successfully. The below table gives the list of courses with a complete overview of all the course contents, description, its links and the time is taken in several hours to complete each course–
Goals
The primary goal of this course is to assist all the trainees in mastering TensorFlow which is a python based library that is used to being the mathematical concepts to implement the features of machine learning. This training will be helping all the trainees to dive deep into all the concepts of TensorFlow which are anyhow associated with this concept. Right after finishing this course, you will be able to work thoroughly with this python-based library to introduce the mathematical functions.
Objectives
The main objective of this course is to endorse the understanding of the trainees on TensorFlow and help them practice all of the concepts. This training is primarily focused on teaching everyone about the practical implementation of the concepts of TensorFlow. One of the important objectives is also to help the trainees become cognizant about working with this library and help them to add extra knowledge regarding python libraries in their knowledgebase.
Course Highlights
This course contains twelve units where some of the units are training units while the other units have the projects.
Machine Learning with Tensorflow is the first unit in this course where we will be learning about all the concepts related to Machine learning. We will understand how Tensorflow helps to implement the concepts of ML in the applications. It will be a thirteen hours long video tutorial which will also be giving you a brief introduction about this library and the problems that could be solved using it.
Deep Learning Tutorials is the next unit and as the name suggests, we will be implementing the concepts of deep learning in this course. It will be around two hours long video tutorial where we have covered the maximum of the concepts that fall under the domain of Deep learning. You will get to learn about all the beginners, medium and advanced level concepts of Deep learning in this course.
Pandas with Python Tutorial is the next important topic that we got covered in this training. We will be diving deep into all the concepts of Pandas with the help of python. Almost all of the topics in this course have been detailed with the help of quick examples which are selected very carefully to make it easy for the trainees to understand the concepts.
NumPy and Pandas will be the other unit in this course and the educator will be detailing all the concepts of both of the topics with the help of precise demonstrations. You will get to learn about how various libraries could be leveraged to solve particular sorts of problems.
Matplotlib for Python Developers – Beginners are the next unit where we will be learning about Matplotlib from a beginner’s point of view. We have included some of the sample questions in this course which will be used as demonstrations to help the trainees dive deep into the concepts.
Matplotlib for Python Developers – Intermediate, Matplotlib for Python Developers – Advanced, Seaborn (3 Courses) are some of the units that we got covered in this course. Once you finish all the units, you will be able to work effectively with all the concepts of TensorFlow.
Project Highlights
There are a total of four projects in this course where all the topics of the project will be different or unique.
Project on Tensorflow – Implementing Linear Model with Python is the first project in this course where we will be working to develop a linear model with the help of python programming language. In this project, we will be using all of the concepts that we would have walked through in the earlier units.
Hands-on Deep Learning Training will be the next project and this project has been included to reinforce your understanding of deep learning. It will be almost a seven and a half hours long video where the educator will be explaining all the topics to you that will be used in this project.
Project on Pandas – Data Management for Retail Dataset is another project in this course. It will help you to understand all the advanced level concepts of Tensorflow. You will be developing the model on data management and will also be learning about error handling. Errors are something that usually comes up when we try to implement the concepts to design solutions for complicated problems.
Case Studies on Seaborn Basics will be the last project in this course which is moreover a case study. The purpose of including this project in the course is to help the trainees get a holistic view of how this library could be used to solve the actual problems.
Certificate of Completion
What is TensorFlow?
The TensorFlow is an open-source library for machine learning and deep learning applications. It is a freeware and does not require a license. TensorFlow was developed by Google Brain Team. TensorFlow was initially released in the year 2015. It was purely written in Python, C++ and CUDA languages. It supports multiple cross platforms such as macOS, Windows, Linux, Android, etc. It is mainly used in the form of a Math library. It was licensed under Apache License 2.0. The usage of Machine Learning contains the classification of basic elements and text, overfitting and underfitting, saving and restoration models. The production scale levels of Machine Learning include linear model, wide and deep learning, boosted trees, estimators based on CNN. The different generative models under TensorFlow are the translation, image captioning, DCGAN and VAE techniques. The different data representation ways in TensorFlow are a vector representation of words, kernel methods, large scale linear models and Unicode.
This TensorFlow is a machine learning platform that is under open source licensing. TensorFlow library can be used for both production and research applications. The different applications that can be carried out under TensorFlow are Research and experimentation, production scale Machine Learning, generative models, Images, Sequences, Load data, data representation, Non-Machine Learning applications.
Industry Growth Trend
The machine learning market is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.[Source - MarketsandMarkets]
Average Salary
Average Salary$141,029 per year
The average salary for a Machine Learning Engineer is $141,029 per year in the United States.
[Source - Indeed]
[Source - Indeed]
Which Skills will you learn in this TensorFlow Course?
- The set of skills that can be acquired upon completion of this TensorFlow training are Data Analysis, TensorFlow, Deep Learning Application, Numpy, Pandas, Matplotlit, Seaborn, Conda Environment, Data Visualization, and Data Processing, etc.
- The TensorFlow course contains also several chapters in the courses and few chapters under each course which was covered in the course curriculum are such as the installation of TensorFlow, Introduction of TensorFlow, different data types in TensorFlow, PyCharm IDE environment setup, etc.
- There are few skills also which could be obtained in completing this course are such as TensorFlow Model, Neural Networks, PyCharm IDE, TensorFlow Eager API, Linear regression, Logistic regression, and TensorFlow, etc.
- The TensorFlow certification also contains a set of tangible skills which are advanced Deep Learning Applications, Statistical Analysis, different mathematical models for prediction analysis, etc.
- This TensorFlow course is very useful for the prospective ML Developers, Machine Learning Engineer, Software Developer, Python Developer, Web Development, etc.
Requirements / Pre-requisites
- Willingness to pursue a career as a Machine Learning Engineer or Analytics Engineer or Hadoop Developer: The TensorFlow training does not contain any prerequisites and can be preferred by any learner to master the basic concepts or knowledge on Machine Learning, Deep learning, data analytics tools, data processing techniques using PyCharm IDE, etc. All the learners who are interested to learn the TensorFlow concepts such as installation and setup of deep learning model using TensorFlow library and python programming etc. Data Analysis and Data Visualization techniques can also be carried out using different tools using the TensorFlow library which is explained in this course. The basic core concepts of the TensorFlow library are explained clearly in the course contents and description. This course can also be learned with any basic knowledge of mathematics or computer science or any basic programming skills or Data analytics or processing tools related technologies.
- Knowledge in Data Analytics or Hadoop or Big Data Tools: Any previous knowledge or hands-on in the areas of Data Analytics or Big Data Development or Hadoop Development or data analytics tools is an added advantage in further learning the contents of the TensorFlow training. This course as several advantages upon learning the core and advanced concepts such as data analysis, TensorFlow library, handling and processing large volumes of data, multiple and parallel operations on large amounts of data in an efficient manner. There are also a lot of benefits to learning the concepts of this TensorFlow course.
Target Audience
- Students of Mathematics or Computer programming or Statistical background: The learners who are holding any Bachelor’s engineering in Computer Science or any technical areas can choose this TensorFlow training as a better option in getting expertise in Deep Learning technologies. All the learners who are keen in learning and obtaining knowledge on Deep Learning techniques or Data processing or Big data analytics or Hadoop frameworks can opt for this TensorFlow course. This TensorFlow training is beneficial and added advantage to master the latest technologies in the technological career or profession with greater benefits to their career and profession. This TensorFlow certification is well recommended to all the prospective learners who are very much interested to learn the latest Data Analysis tools and techniques. This TensorFlow training is designed in a basic and advanced way to cover all the fresh and experienced learners to secure greater job opportunities.
- Analytics Engineer Scientist or Hadoop Developer or Data Architect: The learners of this TensorFlow training will be able to master a lot of basic to advanced concepts in the area of Deep Learning data processing techniques or Data analytics tools or Hadoop related big data concepts, and digital data tools, several Python tools such as PyCharm IDEs and TensorFlow like libraries are added as the contents of this course which are of greater importance in the area of Machine Learning and also adds a lot more benefits to the prospective learner’s career as a Data Analyst, Data Scientist, Software Development Engineer, Software Developer, Research Scientist, Data Analyst, Business Analyst, Hadoop Developer, Researcher, SAS Programmer, R Programmer, Machine Learning Engineer, Machine Learning Developer, AI Expert, Chatbots Developer, AI ML Engineer, Python Developer, Python ML Engineer, Solution Architect, Machine Learning Scientist, etc. This course can opt also to pursue better career opportunities in the area of Machine Learning or Deep learning processes.
- Bachelor / Master’s Degree in Technology: Any learner with Bachelor or Master Degree holder in the technology area can opt for this Edu CBA – TensorFlow course to become a Machine Learning Engineer or Analytics Engineer or Big Data Developer or Data Architect or Specialist in the larger multinational companies.
TensorFlow Course – FAQ’s
Is this TensorFlow training a good one to pursue?
Yes, Any Machine Learning Engineer or Data Architect or Analytics Engineer or Hadoop Developer or prospective Technical Data Processing Engineer who is interested and keen on learning the latest data related technologies can choose this TensorFlow course which is a worthy considerable one.
Can fresher or nontechnical background learners choose this course? Is this good for switching careers in different areas?
YES, this TensorFlow course is a good choice for your profession to switch your career mainstream area. This TensorFlow course can easily be learned and the basic core concepts can be grasped easily which does not need any pre-requisites in the area of computer science basics or advanced technical areas. Anyone learner who is interested in learning the latest data processing or deep learning or machine learning tools and IDEs can opt for this course.
Will this TensorFlow certification provide any benefit/advantage and value to my profession?
Yes. this TensorFlow course is an advantageous and beneficial course for your career in many terms which provides a greater value to your profession in terms of the deep learning or machine learning core concepts and to the profession that includes an extra verifiable certification from the Edu CBA Academy which is a benefit in the professional arena to obtain further responsible roles in the profession.
Is this TensorFlow training a good one to learn about Machine Learning or Deep Learning techniques and tools to prepare for any job interview?
Yes, this TensorFlow course is a good course to learn the latest Deep learning techniques or Machine Learning tools or techniques to prepare for a job interview. Any knowledge of statistics or mathematics or any programming language is also recommended which is highly beneficial to master the contents of this course. This TensorFlow course needs a minimum of 55+ hours to complete.
Career Benefits
- There are several career benefits upon completion of this TensorFlow training and its core and advanced concepts which are mentioned as below:
- This TensorFlow training is a package of two online courses for the setup and installation of the TensorFlow library and its application on different operations such as data analytics, deep learning, machine learning techniques including hands-on video content.
- Any Machine Learning Developer or Deep learning Developer or Big Data Engineer can choose this TensorFlow course to learn the core and advanced data analytics or data processing concepts and tools along with its features concepts to elevate the career to an advanced level such as ML Engineer, Data Analyst, or Analytics Engineer, etc.
- There are many other benefits to the learner’s career upon choosing this TensorFlow certification are such as the TensorFlow course certification which will also be a part of learners’ profile as a verifiably certified qualification.
TensorFlow Course Reviews
Good experience with EDUCBA
The TensorFlow course is the very good one, to begin with, Machine Learning concept and Deep Learning concepts with the coverage of both basic and advanced core concepts such as Linear Regression, Data Analysis, Data processing, which are very well designed in the way of hands-on with good course contents. The number of courses related to Deep Learning and its core concepts is well explained in a good way.
Joshua
Nice Tutorials
The tools in this TensorFlow course are very comprehensive and contain complex mathematical or statistical or neural or deep learning subjects in a crisp manner. Deep learning topics are well articulated that makes the life of learner easy to understand the complex subjects.
Naming Mukherjee
