CNNs with TensorFlow: Basics of Machine Learning from Coursera

In this 90-min long project-based course you will learn how to use Tensorflow to construct neural network models. Specifically, we will design, execute, and evaluate a neural network model to help a retail company with their marketing campaign by classifying images of clothing items into 10 different categories. Throughout this course, you will learn how to use Tensorflow to build and analyze neural neural networks that can perform multi-label classification for applications in image recognition. You will also be able to identify and adapt the main components of neural networks as well as evaluate the performance of different models and implement measures to improve their accuracy. At the end of the project, you will be able to design and implement convolutional neural networks helping a retail store with their targeted ad campaign, and the models can be easily adapted for self-driving cars, computer-assisted medical diagnosis, etc.

This course is aimed at learners who want to get started with the design and implementation of neural networks with an intuitive and effective approach thanks to the Tensorflow library. Computer users with experience with programming in Python should be able to complete the project successfully.

What's inside

Syllabus

Project Overview

In this project-based course you will learn how to use Tensorflow to construct neural network models. Specifically, we will design, execute, and evaluate a neural network model to help a retail company with their marketing campaign by classifying images of clothing items into 10 different categories. Throughout this course, you will learn how to use Tensorflow to build and analyze neural neural networks that can perform multi-label classification for applications in image recognition. You will also be able to identify and adapt the main components of neural networks as well as evaluate the performance of different models and implement measures to improve their accuracy. At the end of the project, you will be able to design and implement convolutional neural networks helping a retail store with their targeted ad campaign, and the models can be easily adapted for self-driving cars, computer-assisted medical diagnosis, etc. This course is aimed at learners who want to get started with the design and implementation of neural networks with an intuitive and effective approach thanks to the Tensorflow library. Basic familiarity with the Python programming language is required. Among the skills needed to complete this project are: importing libraries, defining variables, arrays, functions, and classes, as well as creating plots using the matplotlib library. Basic familiarity with mathematical vectors and matrices is also required. Computer users with programming experience in Python should be able to complete the project successfully.

Good to know

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Explores a particular topic not covered in other courses on the platform

Introduces powerful open-source tools, namely TensorFlow, for advanced machine learning

Core audience consists of those who have basic proficiency with Python and an interest in the niche applications of image recognition

Requires some mathematical knowledge, particularly regarding vectors and matrices, which may pose a barrier to some learners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in CNNs with TensorFlow: Basics of Machine Learning with these activities:

Review programming concepts in Python

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This will help you re-familiarize with basic programming concepts, which will make it easier to grasp the neural network concepts in the course.

Browse courses on Python Programming

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Review variables, data types, and control structures in Python
Practice creating and using functions
Solve simple coding problems using Python

Follow a TensorFlow tutorial

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This will provide you with a structured and hands-on introduction to TensorFlow, the library used in the course for building neural networks.

Browse courses on TensorFlow

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Find a TensorFlow tutorial that covers the basics of neural network construction
Follow the tutorial step-by-step, implementing the code and understanding the concepts
Complete the exercises and assignments in the tutorial

Solve coding problems related to neural networks

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This will help you develop a deeper understanding of neural network architectures and algorithms.

Browse courses on Neural Networks

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Find online coding problems or exercises related to neural networks
Attempt to solve the problems on your own, using the concepts and techniques learned in the course
Check your solutions against the provided answers or discuss them with peers

Four other activities

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Create a resource compilation on neural networks

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This will help you organize and synthesize your learning materials, making them more accessible for future reference and review.

Browse courses on Neural Networks

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Gather relevant resources such as articles, tutorials, and code snippets on neural networks
Organize the resources into different categories or topics
Store the resources in a central location, such as a digital notebook or folder

Create a presentation on a specific neural network application

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This will allow you to explore and present a specific application of neural networks, which will reinforce your understanding and enhance your communication skills.

Browse courses on Neural Networks

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Choose a specific application of neural networks that interests you
Research the application, its architecture, and its benefits
Create a presentation that explains the application, its components, and its potential impact

Attend a workshop on neural network design

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This will provide you with the opportunity to learn from experts in the field and gain practical experience in neural network design.

Browse courses on Neural Networks

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Find a workshop on neural network design that aligns with your interests
Register for the workshop and attend the sessions
Participate actively in the workshop discussions and exercises

Participate in a neural network competition

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This will challenge you to apply your knowledge and skills in a competitive setting, fostering innovation and driving your learning.

Browse courses on Neural Networks

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Find a neural network competition that matches your skill level and interests
Form a team or work individually to develop a solution
Submit your solution to the competition and receive feedback

Career center

Learners who complete CNNs with TensorFlow: Basics of Machine Learning will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

Computer Vision Engineers develop computer vision systems that enable computers to interpret and understand images and videos. This course can help Computer Vision Engineers build a strong foundation in convolutional neural networks, providing them with the necessary skills to develop and implement computer vision solutions for various applications.

See salaries and explore the career path for Computer Vision Engineer

Deep Learning Engineer

Deep Learning Engineers specialize in developing and implementing deep learning models for various applications. This course can help Deep Learning Engineers strengthen their understanding of convolutional neural networks and TensorFlow, enabling them to design and build robust deep learning solutions.

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Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and implement AI systems. This course can help Artificial Intelligence Engineers gain practical experience in developing and deploying neural networks using TensorFlow, enhancing their ability to create effective AI solutions for real-world problems.

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Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models, utilizing machine learning algorithms and techniques to solve business problems. They leverage their knowledge of mathematics and programming to develop innovative solutions. This course can help Machine Learning Engineers build a foundation in TensorFlow and enhance their skills in developing and implementing convolutional neural networks, improving their ability to design and execute effective machine learning models for various applications.

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Research Scientist

Research Scientists conduct research to advance scientific knowledge and develop new technologies. This course can help Research Scientists in the field of machine learning or computer vision expand their knowledge of neural networks and TensorFlow, allowing them to explore new research avenues and contribute to the development of innovative solutions.

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Data Scientist

Data Scientists analyze data to extract valuable insights for businesses. They use their statistical and programming skills to develop models and visualize data. This course can help Data Scientists gain hands-on experience in using TensorFlow to construct and evaluate neural networks, enhancing their ability to derive meaningful insights from data and make informed decisions.

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Business Intelligence Analyst

Business Intelligence Analysts use data analysis to help businesses make informed decisions. They use their knowledge of data and statistics to identify trends and develop recommendations. This course can help Business Intelligence Analysts gain hands-on experience in using TensorFlow for image recognition tasks, enabling them to extract valuable insights from visual data and enhance their decision-making capabilities.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use their programming skills to solve problems and create new solutions. This course can help Software Engineers expand their skillset by introducing them to TensorFlow and the fundamentals of neural network development, enabling them to incorporate these advancements into software applications they create.

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Data Analyst

Data Analysts analyze data to identify trends and patterns. They use their statistical and programming skills to extract insights and communicate them to stakeholders. This course can help Data Analysts gain practical experience in using TensorFlow to perform multi-label classification tasks, enabling them to handle complex data analysis projects more effectively.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They use their skills to make investment recommendations and manage risk. This course can help Quantitative Analysts build a foundation in neural networks and TensorFlow, providing them with the tools to develop and implement innovative financial models.

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Project Manager

Project Managers lead and manage projects from start to finish. They use their project management skills to ensure that projects are completed on time, within budget, and to the required quality. This course can help Project Managers gain an understanding of machine learning and its applications, enabling them to effectively manage and deliver machine learning projects.

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Product Manager

Product Managers lead the development and launch of new products. They use their understanding of customer needs and market trends to create innovative products. This course can help Product Managers gain exposure to machine learning and neural networks, enabling them to make informed decisions about incorporating these technologies into new products and enhance their user experience.

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Technical Writer

Technical Writers create documentation and training materials for technical products. They use their writing and communication skills to convey complex technical information clearly and effectively. This course can help Technical Writers gain an understanding of machine learning and neural networks, enabling them to effectively write documentation and training materials for these technologies.

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Consultant

Consultants provide expert advice and guidance to businesses on various topics. They use their knowledge and experience to help businesses solve problems and achieve their goals. This course can help Consultants gain a basic understanding of machine learning and neural networks, enabling them to provide informed advice to their clients on the potential applications of these technologies.

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Teacher

Teachers educate students in various subjects and at different levels. They use their knowledge, skills, and creativity to impart knowledge and develop students' critical thinking and problem-solving abilities. This course can help Teachers gain a basic understanding of machine learning and neural networks, enabling them to incorporate these topics into their lessons and engage their students in exciting and innovative ways.

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