What you'll get
  • 13+ Hours
  • 1 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Machine learning fundamentals and TensorFlow applications
  • Setting up Python and TensorFlow for development
  • Data handling and visualization with NumPy, Pandas, Matplotlib, and Seaborn
  • Building, training, and deploying TensorFlow models and neural networks
  • Supervised, unsupervised, and deep learning with practical examples
  • Reinforcement learning basics with hands-on implementation
  • Applying TensorFlow effectively in real-world projects

Content

Courses No. of Hours Certificates Details
Machine Learning with Tensorflow for Beginners13h 39m✔View Curriculum

Description

This comprehensive course provides a complete introduction to machine learning using TensorFlow, Google's powerful open-source machine learning library. Designed for beginners and intermediate learners, the course covers fundamental concepts, practical implementation, and hands-on exercises to help participants gain real-world skills.

The curriculum is divided into multiple sections, including:

  • Introduction to Machine Learning: Learn the fundamentals of machine learning, how machines learn, and explore practical applications using TensorFlow.
  • Setting Up Your Workstation: Install Python, TensorFlow, and essential libraries to create an efficient development environment.
  • Python Libraries: Gain proficiency in NumPy for numerical computing, Pandas for data manipulation, and Matplotlib and Seaborn for advanced data visualization.
  • Conda Environments: Create and manage isolated Python environments for different projects.
  • Data Preprocessing: Master techniques such as data cleaning, feature scaling, and handling missing values to prepare datasets for modeling.
  • TensorFlow Basics: Understand tensors, operations, variables, and sessions, and learn to build and execute TensorFlow programs.
  • Building TensorFlow Models: Develop models for regression, classification, clustering, and other machine learning tasks.
  • Neural Networks: Explore neural network architectures, training methods, and evaluation techniques to solve complex problems.

Throughout the course, participants engage in theoretical lessons, practical demonstrations, and hands-on projects. By the end of the course, learners will be able to use TensorFlow to create, train, and deploy machine learning models for tasks like data analysis and prediction.

Requirements

  • A computer (compatible with Mac, Windows, and Linux)

  • No prior TensorFlow knowledge required

  • Basic understanding of machine learning is helpful.

Target Audience

  • Those aiming to pass the TensorFlow Developer exam

  • Students, developers, and data scientists seeking hands-on ML experience

  • Anyone looking to expand skills in AI, machine learning, and deep learning

  • Learners wanting to master building ML models with TensorFlow.