AI Driven TensorFlow Mastery: From Basics to Neural Networks
Learning Path | 1 Course Series
Unlock the potential of AI-driven machine learning with TensorFlow in this comprehensive course. From mastering the basics to advanced techniques, participants will delve into fundamental concepts of machine learning and TensorFlow setup. They'll gain proficiency in essential Python libraries like NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization. With hands-on experience in creating TensorFlow models and neural networks, participants will develop the skills needed to tackle real-world machine learning tasks effectively.
Offer ends in:
What you'll get
- 13+ Hours
- 1 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Fundamentals of machine learning and TensorFlow, including how machines learn and their applications.
- Setup of your development environment for machine learning tasks, including Python and TensorFlow installation.
- Proficiency in essential Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization.
- Creation and execution of TensorFlow programs, building and training TensorFlow models, and designing neural networks for various machine learning tasks.
- Fundamentals of machine learning
- Practical implementation with comprehensive examples of canonical machine learning, and supervised and unsupervised machine learning
- How to identify a problem, select the right model, and optimize it to get the best desired outcome: insights into data
- TensorFlow for deep learning with neural networks
- Deep learning and image-classification examples, and time series predictive model examples
- Reinforcement learning, and how to implement various types with examples
- Effectively use TensorFlow in your production system, including framing a task in each task example
Content
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MODULE 1: Essentials Training
Courses No. of Hours Certificates Details Machine Learning ZERO to HERO - Hands-on with Tensorflow 13h 03m ✔
Description
This comprehensive course offers a thorough introduction to machine learning using TensorFlow, Google's powerful open-source machine learning library. Divided into multiple sections, the curriculum covers fundamental concepts of machine learning, setting up your workstation, essential Python libraries for data manipulation and visualization, TensorFlow basics, and building neural networks. Participants will learn how to set up their development environment, preprocess data, visualize datasets, implement TensorFlow models, and create neural networks for various machine learning tasks.
Introduction to Machine Learning:
Participants are introduced to the fundamentals of machine learning, including the concept of machine learning, how machines learn, and the applications and examples of machine learning using TensorFlow by Google.
Setting up your Workstation:
Participants learn how to set up their development environment for machine learning tasks, including installing Python, TensorFlow, and other necessary libraries.
Numpy:
Participants gain proficiency in using NumPy, a fundamental Python library for numerical computing, for efficient array manipulation and mathematical operations.
Pandas:
Participants learn how to use Pandas, a powerful data manipulation library in Python, for data analysis, manipulation, and cleaning.
Matplotlib:
Participants explore Matplotlib, a popular Python library for data visualization, and learn how to create various types of plots and charts to visualize datasets.
Seaborn:
Participants learn how to use Seaborn, a statistical data visualization library in Python, to create informative and attractive statistical graphics.
Conda Environment:
Participants gain an understanding of Conda environments and learn how to create and manage isolated Python environments for different projects.
Data Visualization:
Participants delve deeper into data visualization techniques, learning advanced plotting and visualization methods using Matplotlib and Seaborn.
Data Preprocessing:
Participants learn essential data preprocessing techniques, including data cleaning, feature scaling, and handling missing values, in preparation for model training.
TensorFlow Basics:
Participants are introduced to the basics of TensorFlow, including tensors, operations, variables, and sessions, and learn how to create and execute simple TensorFlow programs.
TensorFlow Model:
Participants learn how to build and train TensorFlow models for various machine learning tasks, including regression, classification, and clustering.
Neural Networks:
Participants explore neural networks, a powerful machine learning technique inspired by the human brain, and learn how to design, train, and evaluate neural network models using TensorFlow.
Throughout the course, participants engage in theoretical lectures, practical demonstrations, and hands-on exercises to reinforce their learning and develop proficiency in machine learning with TensorFlow. By the end of the course, participants will have the knowledge and skills to leverage TensorFlow for building and deploying machine learning models for a wide range of applications.
Requirements
- Mac / Windows / Linux - all operating systems work with this course!
- No previous TensorFlow knowledge required. Basic understanding of Machine Learning is helpful
Target Audience
- Anyone who wants to pass the TensorFlow Developer exam so they can join Google's Certificate Network and display their certificate and badges on their resume, GitHub, and social media platforms including LinkedIn, making it easy to share their level of TensorFlow expertise with the world
- Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow
- Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning
- Anyone looking to master building ML models with the latest version of TensorFlow
Course Ratings
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Excellent job! Every step was well explained, and I will gladly recommend this course to any beginner.
Ezinne Amadi