TensorFlow Certification Prep

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand neural network architectures
• Understand deep learning frameworks (TensorFlow / PyTorch)

Planned outline shown above (sample metadata). The detailed module and lesson list for this course hasn't been published yet.

Description

A comprehensive course on tensorflow certification prep, covering core concepts, practical applications, and industry best practices.

What this course covers: TensorFlow Certification Prep works through neural network architectures, deep learning frameworks (TensorFlow / PyTorch), working with Python libraries such as NumPy, pandas and scikit-learn and supervised and unsupervised learning.

Skills you will build: preparing data for training, building and evaluating ML models and tuning model performance.

Practical applications: prediction and classification systems, computer vision and NLP applications and AI features inside products.

Who should take this course: developers, data analysts and engineering students moving into machine learning and AI. It is designed for learners with solid experience in machine learning and AI who want advanced, in-depth practice.

Learning outcomes: by the end of the course you should be able to apply neural network architectures and preparing data for training to your own work or projects, and know which areas to practise next.

Note: this course helps you prepare for the topics in the exam syllabus. Certifications are awarded only by the official certifying body, not by this course.

Language: this course is taught in Hindi.

Requirements

• Solid working knowledge of machine learning and AI
• Comfort with intermediate concepts
• A computer with internet access

Your instructor

• M
  Madhuri I. ★★★★★ 4.0/5 Sample review

  The section on working with Python libraries such as NumPy, pandas and scikit-learn was explained step by step, and the examples made supervised and unsupervised learning much easier to follow. The hands-on notebooks made backpropagation finally make sense to me.

• A
  Akhil R. ★★★★★ 4.0/5 Sample review

  Good balance of theory and practice. The deep learning frameworks (TensorFlow / PyTorch) lessons helped me apply building and evaluating ML models in real situations. Loved that model evaluation got as much attention as model building.

• N
  Nikhil E. ★★★★★ 3.5/5 Sample review

  Clear structure from neural network architectures through to deep learning frameworks (TensorFlow / PyTorch). I'd like a few more practice tasks, but overall very useful. The deployment section filled a gap most ML courses skip.

Sample reviews are illustrative demo content, not verified purchases. Ratings from learners will appear here once submitted.