50 Hrs
3293+ Enrollments
TensorFlow Training
This course is suitable for beginners as well as experienced professionals who want to gain expertise in machine learning and TensorFlow. You will learn to use TensorFlow for tasks such as image and speech recognition, natural language processing, and time-series analysis. The course covers various topics such as neural networks, deep learning, and convolutional neural networks.
TensorFlow Training
Overview of Course
Our TensorFlow Training course is designed to equip you with the skills to develop and deploy machine learning models using the TensorFlow framework. With hands-on training, you will gain practical experience in building, training, and deploying deep neural networks for various applications.
Course Highlights
In-depth coverage of TensorFlow and its applications
Hands-on training with real-world datasets
Expert-led training with industry-relevant use cases
Key Differentiators
Personalized Learning with Custom Curriculum
Training curriculum to meet the unique needs of each individual
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Flexible Schedule & Delivery
Choose between virtual/offline with Weekend options
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Enterprise Grade Data Protection
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Real-world Projects
We work with experts to curate real business scenarios as training projects
Skills You’ll Learn
Building and training neural networks using TensorFlow
Deep learning techniques for image and speech recognition
Natural language processing and text analysis
Time-series analysis and prediction
Deploying TensorFlow models for production use
Training Options
1-on-1 Training
Access to live online classes- Flexible schedule including weekends
- Hands-on exercises with virtual labs
- Session recordings and learning courseware included
- 24X7 learner support and assistance
- Book a free demo before you commit!
Corporate Training
- Everything in 1-on-1 Training plus
- Custom Curriculum
- Extended access to virtual labs
- Detailed reporting of every candidate
- Projects and assessments
- Consulting Support
- Training aligned to business outcomes
Unlock Organizational Success through Effective Corporate Training: Enhance Employee Skills and Adaptability- Choose customized training to address specific business challenges and goals, which leads to better outcomes and success.
- Keep employees up-to-date with changing industry trends and advancements.
- Adapt to new technologies & processes and increase efficiency and profitability.
- Improve employee morale, job satisfaction, and retention rates.
- Reduce employee turnovers and associated costs, such as recruitment and onboarding expenses.
- Obtain long-term organizational growth and success.
Course Reviews
Curriculum
- Overview of deep learning and its applications
- Historical background of deep learning
- Types of deep learning algorithms
- Advantages and limitations of deep learning
- Comparison with traditional machine learning
- Introduction to artificial neurons and neural networks
- Activation functions and their role in neural networks
- Backpropagation algorithm and its significance
- Deep neural networks and their architecture
- Regularization techniques to prevent overfitting
- Introduction to deep neural networks
- Deep feedforward networks and their architecture
- Convolutional neural networks (CNNs) and their architecture
- Recurrent neural networks (RNNs) and their architecture
- Deep reinforcement learning and its significance
- Overview of TensorFlow and its components
- TensorFlow installation and setup
- TensorFlow data flow graph and its significance
- TensorFlow variables and constants
- TensorFlow placeholders and their usage
- Introduction to CNNs and their architecture
- Convolutional layers and their role in CNNs
- Pooling layers and their significance
- Implementation of CNNs using TensorFlow
- Applications of CNNs in computer vision
- Introduction to RNNs and their architecture
- Long short-term memory (LSTM) networks and their significance
- Gated recurrent units (GRUs) and their role in RNNs
- Implementation of RNNs using TensorFlow
- Applications of RNNs in natural language processing
- Introduction to RBMs and their architecture
- Contrastive divergence algorithm and its significance
- Introduction to autoencoders and their architecture
- Denoising autoencoders and their significance
- Applications of RBMs and autoencoders in unsupervised learning
- Practice exercises to test the understanding of the concepts learned
- Interview questions to prepare for deep learning-related job interviews
- Discussion of common mistakes and best practices in deep learning
- Tips for further learning and resources to explore
Meet the instructor and learn about the course content and teaching style.- Make informed decisions about whether to enroll in the course or not.
- Get a perspective with a glimpse of what the learning process entails.
Description
Target Audience:
- Data Scientists and Analysts
- Machine Learning Engineers
- Developers and Programmers
- Students and Researchers
Prerequisite:
- Basic knowledge of Python programming
- Understanding of linear algebra and calculus
- Familiarity with machine learning concepts
Benefits of the course:
- Gain expertise in TensorFlow and deep learning techniques
- Practical training with real-world datasets and use cases
- Enhance your career prospects as a machine learning engineer or data scientist
- Lifetime access to course materials and online support
Exam details to pass the course:
- There is no exam to pass the course
- Participants will receive a certificate of completion upon finishing the course
Certification path:
- TensorFlow Developer Certification
- TensorFlow Advanced Certification
Career options:
- Machine Learning Engineer
- Data Scientist
- Research Scientist
- Software Developer
Why should you take this course from Skillzcafe:
Industry-relevant curriculum and training materials- Expert-led training with real-world use cases
- Lifetime access to course materials and online support
- Flexible learning options with self-paced and instructor-led modes
FAQs
Yes, this course is suitable for beginners as well as experienced professionals.
The course duration is approximately 50 hours.
Please check our website for current pricing information.
Yes, you will need to install TensorFlow on your local machine to complete the course exercises.
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