What you'll get
- 63+ Hours
- 16 Courses
- Mock Tests
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
- Download Curriculum
Synopsis
- This course provides comprehensive training on TensorFlow for Deep Learning using Python.
- Learners receive one year of full access to all course materials.
- It is designed for anyone committed to learning Machine Learning and aiming to build a career in this field.
- A basic understanding of Machine Learning concepts is recommended to get the most from the course.
- Participants receive a Certificate of Completion for each course along with hands-on project experience.
- Each certificate is verifiable and comes with a unique link that can be added to resumes or LinkedIn profiles to showcase skills.
- The course is delivered through self-paced video lessons, allowing learners to study at their own convenience.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning with Tensorflow for Beginners | 13h 39m | ✔ | View Curriculum |
| Tensorflow With Python | 1h 46m | ✔ | View Curriculum |
| Project on Tensorflow: Face Mask Detection Application | 33m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Deep Learning with TensorFlow | 3h 11m | ✔ | View Curriculum |
| Comprehensive Deep Learning Training | 11h 17m | ✔ | View Curriculum |
| Deep Learning Tutorials | 1h 34m | ✔ | View Curriculum |
| Pandas with Python Tutorial | 5h 42m | ✔ | View Curriculum |
| Numpy and Pandas | 5h 9m | ✔ | View Curriculum |
| Pandas Project | 3h 14m | ✔ | View Curriculum |
| Matplotlib Basic | 4h 2m | ✔ | View Curriculum |
| Matplotlib Intermediate | 2h 53m | ✔ | View Curriculum |
| Matplotlib Advance | 6h 37m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Seaborn | 2h 28m | ✔ | View Curriculum |
| Seaborn Intermediate | 1h 18m | ✔ | View Curriculum |
| Seaborn Advance | 1h 56m | ✔ | View Curriculum |
| Seaborn Tutorial | 1h 51m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| No courses found in this category. | |||
Description
This TensorFlow training equips learners with a wide range of skills, including Data Analysis, Deep Learning applications, the TensorFlow framework, and essential Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn. Participants also gain expertise in data visualization, data processing, and Conda environment management.
The course is structured into multiple comprehensive modules covering key topics such as TensorFlow installation, introduction to TensorFlow, understanding different data types, and setting up the PyCharm IDE environment. Learners also acquire practical skills in building TensorFlow models, designing neural networks, using the TensorFlow Eager API, and implementing linear and logistic regression techniques.
Beyond foundational skills, the course emphasizes advanced capabilities, including Deep Learning applications, statistical analysis, and the application of mathematical models for predictive analytics.
This TensorFlow certification is particularly valuable for aspiring Machine Learning Developers, Machine Learning Engineers, Software Developers, Python Developers, and Web Development professionals seeking to enhance their expertise and career prospects.
Sample Certificate
Requirements
- Learners should have a strong interest in building a career as a Machine Learning Engineer, Analytics Engineer, Hadoop Developer, or in any related data-focused role.
- The course has no mandatory prerequisites and is suitable for anyone aiming to understand the fundamentals of Machine Learning, Deep Learning, data analytics tools, and data processing techniques using the PyCharm IDE.
- It is ideal for individuals who want to learn TensorFlow concepts, including installation, environment setup, and building deep learning models using Python.
- A basic understanding of mathematics, computer science, programming, or data analytics tools is helpful but not required.
- Prior exposure to Data Analytics, Big Data technologies, or Hadoop can provide an additional advantage in grasping advanced concepts more quickly.
- Familiarity with data handling, managing large datasets, or performing parallel operations can further enhance the learning experience in this course.
Target Audience
- Suitable for students and graduates in mathematics, statistics, computer science, or engineering who want to build skills in Deep Learning and data technologies.
- Ideal for individuals interested in learning TensorFlow, data processing, Big Data analytics, or Hadoop frameworks.
- Beneficial for both beginners and professionals aiming to strengthen their expertise in modern Data Analysis and Machine Learning tools.
- A great fit for roles such as Data Scientist, Machine Learning Engineer, Analytics Engineer, Hadoop Developer, Software Developer, and AI/ML Specialist.
- Recommended for learners with a Bachelor's or Master's degree in technology looking to advance their careers in Machine Learning or Big Data domains.
