AI Driven TensorFlow Mastery: From Basics to Neural Networks
Learning Path | 1 Course Series

Synopsis

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.