TensorFlow: Basic to Advanced Training

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1 - Introduction to Machine Learning and TensorFlow

1 -What is Machine Learning.mp4

10:59

2 -Introduction to TensorFlow.mp4

07:53

3 -TensorFlow vs. Other Machine Learning frameworks.mp4

15:11

4 -Installing TensorFlow.mp4

11:46

5 -Setting up your Development Environment.mp4

09:39

6 -Verifying the Installation.mp4

13:33

2 - Basics of TensorFlow

1 -Introduction to Tensors.mp4

02:18

2 -Tensor Operations.mp4

04:28

3 -Constants, Variables, and Placeholders.mp4

03:41

4 -TensorFlow Computational Graph.mp4

04:28

5 -Creating and Running a TensorFlow Session.mp4

03:09

6 -Managing Graphs and Sessions.mp4

04:37

7 -Building a Simple Feedforward Neural Network.mp4

05:31

8 -Activation Functions.mp4

04:31

9 -Loss Functions and Optimizers.mp4

06:20

3 - Intermediate TensorFlow

1 -Introduction to Keras API.mp4

05:17

2 -Building Complex Models with Keras.mp4

04:47

3 -Training and Evaluating Models.mp4

05:19

4 -Introduction to CNNs(Convolutional Neural Networks).mp4

04:59

5 -Building and Training CNNs with TensorFlow.mp4

03:32

6 -Transfer Learning with Pre-trained CNNs.mp4

05:25

7 -Introduction to RNNs(Recurrent Neural Networks).mp4

05:17

8 -Building and Training RNNs with TensorFlow.mp4

03:29

9 -Applications of RNNs Language Modeling, Time Series Prediction.mp4

03:38

4 - Advanced TensorFlow

1 -Saving and Loading Models.mp4

04:32

2 -TensorFlow Serving for Model Deployment.mp4

04:29

3 -TensorFlow Lite for Mobile and Embedded Devices.mp4

05:28

5 -TensorFlows Distributed Execution Framework.mp4

05:34

6 -Scaling TensorFlow with TensorFlow Serving and Kubernetes.mp4

05:52

7 -Introduction to TFX(TensorFlow Extended).mp4

06:07

8 -Building End-to-End ML Pipelines with TFX.mp4

04:12

9 -Model Validation, Transform, and Serving with TFX.mp4

05:32

5 - Practical Applications and Projects

1 -Image Classification.mp4

05:56

2 -Natural Language Processing.mp4

05:37

3 -Recommender Systems.mp4

05:52

4 -Object Detection.mp4

05:07

5 -Building a Sentiment Analysis Model.mp4

06:03

6 -Creating an Image Recognition System.mp4

05:03

7 -Developing a Time Series Prediction Model.mp4

04:04

8 -Implementing a Chatbot.mp4

05:54

6 - Further Learning and Resources

1 -Generative Adversarial Networks (GANs).mp4

05:06

2 -Reinforcement Learning with TensorFlow.mp4

05:41

3 -Quantum Machine Learning with TensorFlow Quantum.mp4

05:27

4 -TensorFlow Documentation and Tutorials.mp4

04:37

5 -Online Courses and Books.mp4

03:07

6 -TensorFlow Community and Forums.mp4

04:16

7 - Summary of Tensor Flow

1 -Summary of Key Concepts.mp4

05:21

2 -Next Steps in Your TensorFlow Journey.mp4

04:26

Description

Flexible, Scalable, Open-Source Machine Learning Framework

What You'll Learn?

Core TensorFlow concepts from setup to model building, enabling them to confidently create machine learning projects.
Techniques for building CNNs and RNNs for image, language, and sequence data, equipping them to tackle various ML problems.
Skills to deploy TensorFlow models to production, including scaling with distributed computing and deploying on mobile.
Practical experience with real-world ML applications, building models for image recognition, sentiment analysis, and more.

Who is this for?

Aspiring Data Scientists and ML Engineers who want to build a solid foundation in TensorFlow for real-world machine learning projects

Developers and Programmers interested in expanding their skills to include machine learning and neural networks

Students and Professionals in data science, AI, or related fields, looking to add TensorFlow to their toolkit

Self-Learners who enjoy hands-on projects and are ready to dive into practical, scalable applications in machine learning

What You Need to Know?

Basic programming knowledge, ideally in Python

Understanding of fundamental math concepts like linear algebra and probability

Familiarity with machine learning basics is helpful but not required

A computer with internet access for installing TensorFlow and coding projects

More details

Description
This course offers a comprehensive journey into TensorFlow, guiding learners from the basics to advanced applications of machine learning and deep learning with this powerful open-source framework. Starting with an introduction to machine learning and the unique capabilities of TensorFlow, students will gain foundational knowledge that sets the stage for more complex concepts. The course begins with installation and setup instructions to ensure every student is equipped with the necessary tools and environment for TensorFlow development. Early modules cover the essential building blocks of TensorFlow, including tensors, operations, computational graphs, and sessions. Through these topics, students will understand the core components of TensorFlow and how to utilize them effectively for simple projects and data operations.
As the course progresses, learners dive deeper into neural networks, exploring how to build, train, and optimize basic models. The intermediate section introduces Keras, the user-friendly API for TensorFlow, allowing students to design and train complex models more intuitively. Topics like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) provide hands-on experience with real-world data types, such as images and sequences. The course then transitions to advanced topics, covering essential skills for deploying and scaling models. Students will learn to save, load, and serve TensorFlow models, enabling them to apply their knowledge in production environments. Theyâ€™ll also explore distributed TensorFlow for scaling applications across multiple devices and TensorFlow Extended (TFX) for building end-to-end machine learning pipelines.
With practical projects and real-world applications woven throughout, students will have the chance to build models for tasks like image classification, sentiment analysis, and time series prediction, solidifying their skills through hands-on practice. By the end of the course, learners will be equipped not only with the technical knowledge but also the practical experience needed to implement, deploy, and manage TensorFlow models in professional environments. This course is ideal for anyone looking to advance their career in data science, machine learning, or artificial intelligence, empowering them with the expertise to tackle complex challenges in todayâ€™s data-driven world.
Who this course is for:
Aspiring Data Scientists and ML Engineers who want to build a solid foundation in TensorFlow for real-world machine learning projects
Developers and Programmers interested in expanding their skills to include machine learning and neural networks
Students and Professionals in data science, AI, or related fields, looking to add TensorFlow to their toolkit
Self-Learners who enjoy hands-on projects and are ready to dive into practical, scalable applications in machine learning

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