TensorFlow Mastery- Unleashing the Power of Machine Learning

TensorFlow Mastery: Unleashing the Power of Machine Learning, available at $54.99, has an average rating of 4.88, with 110 lectures, based on 8 reviews, and has 4412 subscribers.

You will learn about Understand the fundamentals of Machine Learning and TensorFlow. Set up your workstation and explore third-party libraries for data analysis. Master essential concepts like NumPy, Pandas, data visualization, and Seaborn. Learn about California datasets, data visualization, and processing with Scikit Learn. Delve into linear regression, fine-tuning models, and TensorFlow basics. Explore advanced topics, including logistic regression and neural networks. Apply your knowledge through hands-on projects, such as face mask detection and linear model implementation. Develop practical skills for real-world machine learning applications. This course is ideal for individuals who are Anyone who wants to pass the TensorFlow Developer exam so they can join Googles 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 or Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow or Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning or Anyone looking to master building ML models with the latest version of TensorFlow It is particularly useful for Anyone who wants to pass the TensorFlow Developer exam so they can join Googles 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 or Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow or Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning or Anyone looking to master building ML models with the latest version of TensorFlow.

Enroll now: TensorFlow Mastery: Unleashing the Power of Machine Learning

Summary

Title: TensorFlow Mastery: Unleashing the Power of Machine Learning

Price: $54.99

Average Rating: 4.88

Number of Lectures: 110

Number of Published Lectures: 110

Number of Curriculum Items: 110

Number of Published Curriculum Objects: 110

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the fundamentals of Machine Learning and TensorFlow.

Set up your workstation and explore third-party libraries for data analysis.

Master essential concepts like NumPy, Pandas, data visualization, and Seaborn.

Learn about California datasets, data visualization, and processing with Scikit Learn.

Delve into linear regression, fine-tuning models, and TensorFlow basics.

Explore advanced topics, including logistic regression and neural networks.

Apply your knowledge through hands-on projects, such as face mask detection and linear model implementation.

Develop practical skills for real-world machine learning applications.

Who Should Attend

Anyone who wants to pass the TensorFlow Developer exam so they can join Googles 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

Target Audiences

Anyone who wants to pass the TensorFlow Developer exam so they can join Googles 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

Immerse yourself in the cutting-edge world of deep learning with TensorFlow through this comprehensive masterclass. Starting with an insightful overview and the scenario of perceptron, progress to creating neural networks, performing multiclass classification, and gaining a deep understanding of convolutional neural networks (CNN). Explore image processing, convolution intuition, and classifying photos of dogs and cats using TensorFlow. Understand the layers of deep learning neural networks and harness the power of transfer learning for advanced concepts. Engage in real-world projects like Face Mask Detection and Linear Model Implementation. Elevate your skills to master TensorFlow, enabling you to build and deploy powerful deep learning models.

This masterclass is designed for individuals passionate about deep learning, whether beginners or experienced practitioners. Uncover the secrets of TensorFlow and take your understanding of deep learning to new heights!

Section 1: Machine Learning ZERO to HERO – Hands-on with TensorFlow

This foundational section serves as a comprehensive introduction to machine learning using TensorFlow. It begins with essential concepts, including understanding the fundamentals of machine learning and how machines learn. The section then progresses to practical aspects, guiding learners through setting up their workstations, exploring different programming languages, and understanding the functions of Jupyter notebooks. The focus expands to include third-party libraries, with an emphasis on NumPy and Pandas for efficient data manipulation and analysis. The section concludes by introducing data visualization using Matplotlib and Seaborn, providing a solid groundwork for the subsequent sections.

Section 2: Project On TensorFlow – Face Mask Detection Application

In this hands-on project section, learners apply their knowledge to a real-world application by building a Face Mask Detection application using TensorFlow. The project covers various crucial steps, starting with package installation and moving through data loading and preprocessing, model training, saving and loading models, and creating functions for predictions. The section’s practical nature allows learners to actively engage with the material, reinforcing their understanding of TensorFlow in a tangible project.

Section 3: Project on TensorFlow – Implementing Linear Model with Python

Continuing the practical approach, this section focuses on another project where learners implement a linear model using TensorFlow with Python. The content covers the installation of TensorFlow, basic data types, creating a simple linear model, and optimizing variables. The hands-on experience extends to creating Python files and printing variable results, providing learners with a deeper understanding of TensorFlow in action.

Section 4: Deep Learning: Automatic Image Captioning For Social Media With TensorFlow

Transitioning into the realm of deep learning, this section explores a specific application: automatic image captioning for social media using TensorFlow. Learners dive into practical aspects such as accessing and preprocessing caption and image datasets, creating data generators, defining models, and evaluating model performance. The section concludes with a focus on practical deployment, guiding learners through creating a Streamlit app, testing it, and deploying it on an AWS EC2 instance.

Section 5: Conclusion and Advanced Concepts

The final section serves as both a recap of the entire course and an introduction to advanced concepts in TensorFlow. It revisits essential TensorFlow operations and covers topics like linear regression, logistic regression, and the basics of neural networks. Practical examples are integrated throughout the lectures, ensuring learners gain hands-on experience with the concepts covered throughout the course. This concluding section aims to solidify learners’ understanding and prepare them for further exploration of advanced TensorFlow concepts.

Course Curriculum

Chapter 1: Machine Learning ZERO to HERO – Hands-on with Tensorflow

Lecture 1: Introduction to Machine Learning with Tensorflow

Lecture 2: Understanding Machine Learning

Lecture 3: How do Machines Learns

Lecture 4: Uses of Machine Learning

Lecture 5: Examples with tensorflow by Google

Lecture 6: Setting up the Workstation

Lecture 7: Understanding program languages

Lecture 8: Understanding and Functions of Jupyter

Lecture 9: Learning of Jupyter installation

Lecture 10: Understanding what Anaconda cloud is

Lecture 11: Installation of Anaconda for Windows

Lecture 12: Installation of Anaconda in Linux

Lecture 13: Using the Jupyter notebook

Lecture 14: Getting started with Anaconda

Lecture 15: Determining options for Cloudberry

Lecture 16: Introduction to Third Party Libraries

Lecture 17: Numpy-Array

Lecture 18: Numpy-Array Continue

Lecture 19: Arrays

Lecture 20: Arrays Continue

Lecture 21: Indexing

Lecture 22: Indexing Continue

Lecture 23: Universal Functions

Lecture 24: Introoduction to Pandas

Lecture 25: Pandas Series

Lecture 26: Pandas Series Continue

Lecture 27: Import Randin

Lecture 28: Import Randin Continue

Lecture 29: Paratmeters

Lecture 30: Indexing and Database

Lecture 31: Missing Data

Lecture 32: Missing Data-Groupby

Lecture 33: Missing Data-Groupby Continue

Lecture 34: Concat-Merge-Join

Lecture 35: Operations

Lecture 36: Import-Export

Lecture 37: Python Visualisation

Lecture 38: Mat Plotting

Lecture 39: Multiple Plot Subsections

Lecture 40: API Functionality

Lecture 41: Title of the Plot

Lecture 42: Change Size of Articles

Lecture 43: Two Different Crops

Lecture 44: Mat Plotting Label

Lecture 45: Marker Color

Lecture 46: Create a New Dataframe

Lecture 47: Change the Style

Lecture 48: Index and Value

Lecture 49: Seaborn-Statistical Data Visualization

Lecture 50: Seaborn library

Lecture 51: Jointplot

Lecture 52: Pairplot

Lecture 53: Barplot

Lecture 54: Boxplot

Lecture 55: Stripplot

Lecture 56: Matrix

Lecture 57: Matrix Continue

Lecture 58: Grid

Lecture 59: Grid Continue

Lecture 60: Style

Lecture 61: Python Libraries Conclusion

Lecture 62: Introduction To Conda Envirement

Lecture 63: Scikit Learn

Lecture 64: Scikit Learn Continue

Lecture 65: Datasets

Lecture 66: California Dataset

Lecture 67: Data Visualization

Lecture 68: Datavisualization Continue

Lecture 69: Downloading a Test Data

Lecture 70: Population Parameter

Lecture 71: Processing

Lecture 72: Null Values with Median Value

Lecture 73: Replace Missing Values

Lecture 74: Label Enconder

Lecture 75: Import Labelencoder

Lecture 76: Custom Transformation

Lecture 77: Transformer Custom Transformer

Lecture 78: Housing with Custom Colums

Lecture 79: Numeric Hosing Data

Lecture 80: Liner Regression

Lecture 81: Fine Tuning Model

Lecture 82: Fine Tuning Model Continue

Lecture 83: Quick-Recap

Lecture 84: Tensorflow

Lecture 85: Tensorflow-Hello-World

Lecture 86: Basic Ops

Lecture 87: Basic Ops Continue

Lecture 88: More on Basic Ops

Lecture 89: Eager-Mode

Lecture 90: Concept

Lecture 91: Linear-Regression

Lecture 92: Linear-Model

Lecture 93: Matrix Multiplication Function

Lecture 94: Practice for a Simple Linear Model

Lecture 95: Cost Function

Lecture 96: Creative Optimizer

Lecture 97: RR Input and Output Value

Lecture 98: Logistic-Regression

Lecture 99: Global Variabales Initializer

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

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