Practical Neural Networks and Deep Learning in Python from Udemy

It is a full 5-Hour+ Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Python Deep Learning frameworks- PyTorch, H2O, Keras & Tensorflow.

This means, this course covers the important aspects of these architectures and if you take this course, you can do away with taking other courses or buying books on the different Python-based- deep learning architectures.

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of frameworks such as PyTorch, Keras, H2o, Tensorflow is revolutionizing Deep Learning...

By gaining proficiency in PyTorch, H2O, Keras and Tensorflow, you can give your company a competitive edge and boost your career to the next level.

THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL

But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.

Over the course of my research, I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.

This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the PyTorch, H2O, Tensorflow and Keras framework.

Unlike other Python courses and books, you will actually learn to use PyTorch, H20, Tensorflow and Keras on real data. Most of the other resources I encountered showed how to use PyTorch on in-built datasets which have limited use.

THIS ISN'

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real -life.

After taking this course, you’ll easily use packages like Numpy, Pandas, and PIL to work with real data in Python along with gaining fluency in the most important of deep learning architectures. I will even introduce you to deep learning models such as Convolution Neural network (CNN) .

The underlying motivation for the course is to ensure you can apply Python-based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, the majority of the course will focus on implementing different techniques on real data and interpret the results. Some of the problems we will solve include identifying credit card fraud and classifying the images of different fruits.

After each video, you will learn a new concept or technique which you may apply to your own projects.

What's inside

Learning objectives

Harness the power of anaconda/ipython for practical data science (including ai applications)
Learn how to install & use important deep learning packages within anaconda (including keras, h20, tensorflow and pytorch)

Implement statistical & machine learning techniques with tensorflow
Implement neural network modelling with deep learning packages including keras

Harness the power of anaconda/ipython for practical data science (including ai applications)
Learn how to install & use important deep learning packages within anaconda (including keras, h20, tensorflow and pytorch)
Implement statistical & machine learning techniques with tensorflow
Implement neural network modelling with deep learning packages including keras

Syllabus

Introduction to the Course

Introduction

Data and Scripts

Why Artificial Intelligence and Deep Learning?

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Builds a strong foundation in artificial neural networks, deep neural networks, and deep learning models such as convolutional neural networks

Emphasizes implementation on real data, equipping learners with practical skills

Covers a range of deep learning frameworks (PyTorch, H2O, Keras, and Tensorflow), increasing adaptability

Led by instructor Minerva Singh, who possesses a strong academic background in geography, environment, tropical ecology, and conservation

Requires familiarity with NumPy and Pandas, which may necessitate additional learning for some students

Intended for learners interested in implementing Python-based data science and deep learning techniques on real-world data

Reviews summary

Practical deep learning with multiple frameworks

According to learners, this course provides a solid practical foundation in neural networks and deep learning, highly valued for its focus on real-world applications. Students commend the hands-on approach and the instructor's clear explanations. The broad framework coverage (PyTorch, TensorFlow, etc.) offers a useful overview. However, some find the pace challenging for beginners, suggesting prior Python and ML knowledge helps. A key concern from recent reviews is that certain sections appear outdated due to library changes, requiring troubleshooting. Despite this, it's considered valuable for skill development, often serving as a good starting point.

Comprehensive coverage of multiple popular deep learning frameworks.

"The breadth is impressive, covering Keras, PyTorch, TensorFlow, and H2O, which allowed me to compare them."

"I gained a good overview of the different deep learning frameworks, which is exactly what I was looking for as a starting point."

"This course provided a valuable introduction to various deep learning frameworks, helping me understand their differences."

Instructor provides easy-to-understand, relevant explanations.

"The instructor's explanations are clear and the examples are relevant."

"I found the course easy to follow, even for complex topics, thanks to the instructor."

"The instructor simplifies difficult concepts, making the learning experience very accessible."

Promotes active learning through practical coding exercises.

"The hands-on coding and projects are the strongest part of the course for me, especially the CNN section."

"I found the methods easy-to-understand and hands-on, simplifying even the most difficult concepts effectively."

"This course provides practical tools and strategies I could immediately apply to my work by working with real data."

Course emphasizes practical applications using real-world data.

"This course is truly practical! The focus on real-world data and applications with PyTorch, Keras, and TensorFlow made all the difference."

"I really valued the focus on real data and the practical application of the concepts. This isn't just theory; it's about getting my hands dirty with actual problems."

"I learned how to apply Python-based data science on real data into practice today, which is exactly what I needed for my career."

Fast-paced, ideally suited for intermediate learners.

"While the course provides good practical examples, it sometimes felt rushed, and I needed to consult external resources for deeper understanding."

"If you're a complete beginner, you might find the pace challenging. Some prerequisites in Python and basic ML would be beneficial."

"I felt the course moved very quickly through new concepts, which was tough without prior knowledge."

Some sections and libraries may require updating or troubleshooting.

"Some sections felt a bit outdated in terms of library versions or best practices, requiring extra effort to troubleshoot."

"The code examples didn't always run without modification due to library version mismatches. It took extra effort to troubleshoot."

"I wish there were more updated content or notes on version changes for the deep learning frameworks."

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 Practical Neural Networks and Deep Learning in Python with these activities:

Organize Course Materials

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Organize your notes, assignments, and resources to facilitate effective review and retention.

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Create a digital or physical folder to store all course materials
Regularly review and update your notes
Color-code or tag different topics for easy retrieval

Connect with Industry Experts

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Seek guidance from professionals in the field to gain valuable insights and expand your network.

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Identify potential mentors through LinkedIn or industry events
Craft a personalized message expressing your interest
Schedule a meeting or set up a virtual coffee chat

Form a Study Group

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Collaborate with peers to discuss course concepts, solve problems, and prepare for assessments.

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Find like-minded individuals who share your interests and goals
Set regular meeting times and establish a study schedule
Take turns leading discussions and presenting on different topics

Five other activities

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Show all eight activities

Practice Python Basics

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Practice basic Python syntax and operations to strengthen your understanding of the language.

Browse courses on Python Basics

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Write a simple Python script to print "Hello, data science!"
Create a Python list and perform basic operations on it (e.g., append, remove, sort)
Write a loop to iterate over a Python dictionary and print key-value pairs

Provide Support to Fellow Learners

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Share your knowledge and assist other students in the course by answering questions or providing guidance.

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Join online forums or discussion boards related to the course
Contribute by sharing your insights and experiences
Offer assistance to other learners who may be facing challenges

Explore Deep Learning Resources

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Engage with online resources and tutorials to supplement your learning and stay updated on best practices.

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Find and follow tutorials on platforms like Coursera, Udemy, or edX
Subscribe to YouTube channels or blogs that cover deep learning
Read research papers on deep learning algorithms and applications

Exercise Deep Learning Models

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Train and evaluate deep learning models using different frameworks to enhance your practical skills.

Browse courses on Deep Learning Models

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Build a simple neural network using PyTorch or TensorFlow
Train the model on a given dataset
Evaluate the model's performance using metrics like accuracy and loss

Develop a Data Science Portfolio

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Create a showcase of your deep learning projects and demonstrate your skills to potential employers or collaborators.

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Choose a specific focus area within deep learning
Work on projects that align with your focus and showcase your abilities
Document your projects, including code, results, and insights

Career center

Learners who complete Practical Neural Networks and Deep Learning in Python will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

A Deep Learning Engineer specializes in developing and deploying deep learning models for various applications, such as image recognition, natural language processing, and predictive analytics. This course provides a comprehensive overview of deep learning architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), using Python and frameworks like PyTorch, Keras, and Tensorflow. You'll learn the fundamentals of deep learning and how to apply them to real-world problems, positioning yourself for success as a Deep Learning Engineer.

See salaries and explore the career path for Deep Learning Engineer

Data Scientist

A Data Scientist leverages algorithms and machine learning techniques to analyze data, extract meaningful insights, and help businesses make informed decisions. This course provides a comprehensive foundation in data science, including machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. It covers key concepts such as data preparation, model building, and evaluation, empowering you to tackle real-world data science challenges and advance your career as a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models to solve complex problems and automate tasks. This course offers a practical approach to machine learning, deep learning, and neural networks using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these technologies, you can build and implement effective machine learning solutions, enhancing your competitiveness as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Researcher

An Artificial Intelligence Researcher develops and investigates new artificial intelligence techniques and algorithms. This course provides a comprehensive overview of machine learning, deep learning, and neural networks, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools and techniques, you'll be able to contribute to the advancement of artificial intelligence and position yourself for a successful career as an Artificial Intelligence Researcher.

See salaries and explore the career path for Artificial Intelligence Researcher

Computer Vision Engineer

A Computer Vision Engineer develops and deploys computer vision models for various applications, such as image recognition, object detection, and video analysis. This course offers a comprehensive overview of deep learning architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), using Python and frameworks like PyTorch, Keras, and Tensorflow. You'll learn the fundamentals of computer vision and how to apply them to real-world problems, positioning yourself for success as a Computer Vision Engineer.

See salaries and explore the career path for Computer Vision Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer develops and deploys natural language processing models for various applications, such as machine translation, text summarization, and question answering. This course offers a comprehensive overview of deep learning architectures, including Recurrent Neural Networks (RNNs) and Transformers, using Python and frameworks like PyTorch, Keras, and Tensorflow. You'll learn the fundamentals of natural language processing and how to apply them to real-world problems, positioning yourself for success as a Natural Language Processing Engineer.

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Data Analyst

A Data Analyst collects, analyzes, and interprets data to provide valuable insights and support decision-making. This course offers a strong foundation in data science, including machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By mastering these tools and techniques, you'll be able to extract meaningful information from data, enabling you to excel as a Data Analyst.

See salaries and explore the career path for Data Analyst

Statistician

A Statistician collects, analyzes, and interprets data to extract meaningful insights. This course provides a solid foundation in machine learning, deep learning, and statistical techniques, using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By mastering these tools, you'll be able to develop and implement sophisticated statistical models, enhancing your competitiveness as a Statistician.

See salaries and explore the career path for Statistician

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical techniques to analyze financial data and make investment decisions. This course offers a robust grounding in machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools, you'll be able to develop and implement sophisticated quantitative models, giving you an edge as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Data Science Manager

A Data Science Manager leads and manages a team of data scientists and analysts to achieve organizational goals. This course offers a comprehensive overview of data science, including machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools and techniques, you'll be able to provide strategic direction, manage projects, and effectively lead a data science team.

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Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course provides a practical approach to machine learning, deep learning, and neural networks using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By incorporating these technologies into your software development skills, you can enhance the capabilities of your applications and stay competitive in the tech industry.

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Actuary

An Actuary assesses and manages financial risks using mathematical and statistical techniques. This course provides a solid foundation in machine learning, deep learning, and statistical techniques, using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By mastering these tools, you'll be able to develop and apply sophisticated risk models, enhancing your competitiveness as an Actuary.

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Business Analyst

A Business Analyst bridges the gap between business and technology by analyzing business needs and designing solutions. This course offers a comprehensive overview of data science, including machine learning, deep learning, and statistical techniques, using Python and popular frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools and techniques, you'll be able to provide data-driven insights and support decision-making, propelling your career as a Business Analyst.

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Computational Neuroscientist

A Computational Neuroscientist uses computational techniques to understand the brain and nervous system. This course provides a solid foundation in machine learning, deep learning, and statistical techniques, using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By mastering these tools and techniques, you'll be able to develop and implement sophisticated models of neural systems, enhancing your competitiveness as a Computational Neuroscientist.

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Consultant

A Consultant provides professional advice and guidance to clients on a variety of business and technical issues, typically as an independent contractor. This course may be useful for those interested in a career as a Consultant as it provides a broad overview of data science, machine learning, and deep learning techniques using Python and frameworks like PyTorch, H2O, Keras, and Tensorflow. By gaining proficiency in these tools and techniques, you'll be able to offer data-driven solutions and insights to clients, enhancing your competitiveness as a Consultant in the tech industry.

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