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In the ever-evolving realm of machine learning and artificial intelligence, TensorFlow stands out as an open-source library that has revolutionized the way algorithms are designed and deployed. Developed by the Google Brain team, TensorFlow has become synonymous with creating sophisticated neural networks and deep learning models. Whether you're a budding data scientist, a seasoned AI practitioner, or simply an enthusiast looking to delve into the world of machine learning, mastering TensorFlow is a step towards the future.
The online education ecosystem is brimming with courses that cater to learners at various levels of expertise in TensorFlow. From comprehensive beginner-friendly tutorials to advanced sessions on complex model tuning, there's a course out there for everyone. However, with such an abundance of resources, finding the right course can be as daunting as the subject matter itself.
To aid you on your journey, we have meticulously curated a list of the 10 best TensorFlow courses available online. These courses have been selected based on their comprehensiveness, quality of instruction, practical value, learner feedback, and their ability to keep pace with the latest advancements in TensorFlow technology. Whether you aim to harness the capabilities of TensorFlow for personal projects or to elevate your professional skill set, these courses will guide you through the intricacies of this powerful tool, ensuring a deep understanding and hands-on experience.
Top 10 TensorFlow Courses Online
Get More Details On Each TensorFlow Course:
TensorFlow Developer Certificate: Zero to Mastery
★★★★★
$15.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Pass the TensorFlow Developer Certification Exam by Google. Become an AI, Machine Learning, and Deep Learning expert!
Created by: Andrei Neagoie
Founder of zerotomastery.io
Founder of zerotomastery.io
Created by: Daniel Bourke
Machine Learning Engineer/Writer/Video maker
Machine Learning Engineer/Writer/Video maker
Rating:4.68 (9408reviews)
65927students enrolled
What Will I Learn?
- Learn to pass Google's official TensorFlow Developer Certificate exam (and add it to your resume)
- Build TensorFlow models using Computer Vision, Convolutional Neural Networks and Natural Language Processing
- Complete access to ALL interactive notebooks and ALL course slides as downloadable guides
- Increase your skills in Machine Learning and Deep Learning, to test your abilities with the TensorFlow assessment exam
- Understand how to integrate Machine Learning into tools and applications
- Learn to build all types of Machine Learning Models using the latest TensorFlow 2
- Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks
- Using real world images to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
- Applying Deep Learning for Time Series Forecasting
- Gain the skills you need to become a TensorFlow Certified Developer
- Be recognized as a top candidate for recruiters seeking TensorFlow developers
Requirements
- Mac / Windows / Linux - all operating systems work with this course!
- No previous TensorFlow knowledge required. Basic understanding of Machine Learning is helpful
Target audience
- Anyone who wants to pass the TensorFlow Developer exam so they can join Google's 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
Tensorflow 2.0: Deep Learning and Artificial Intelligence
★★★★★
$23.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!
Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Artificial intelligence and machine learning engineer
Created by: Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Artificial Intelligence and Machine Learning Engineer
Rating:4.73 (10757reviews)
50870students enrolled
What Will I Learn?
- Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
- Predict Stock Returns
- Time Series Forecasting
- Computer Vision
- How to build a Deep Reinforcement Learning Stock Trading Bot
- GANs (Generative Adversarial Networks)
- Recommender Systems
- Image Recognition
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Use Tensorflow Serving to serve your model using a RESTful API
- Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
- Use Tensorflow's Distribution Strategies to parallelize learning
- Low-level Tensorflow, gradient tape, and how to build your own custom models
- Natural Language Processing (NLP) with Deep Learning
- Demonstrate Moore's Law using Code
- Transfer Learning to create state-of-the-art image classifiers
- Earn the Tensorflow Developer Certificate
- Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion
Requirements
- Know how to code in Python and Numpy
- For the theoretical parts (optional), understand derivatives and probability
Target audience
- Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0
Complete Tensorflow 2 and Keras Deep Learning Bootcamp
★★★★★
$15.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!
Created by: Jose Portilla
Head of Data Science at Pierian Training
Head of Data Science at Pierian Training
Rating:4.63 (7937reviews)
48796students enrolled
What Will I Learn?
- Learn to use TensorFlow 2.0 for Deep Learning
- Leverage the Keras API to quickly build models that run on Tensorflow 2
- Perform Image Classification with Convolutional Neural Networks
- Use Deep Learning for medical imaging
- Forecast Time Series data with Recurrent Neural Networks
- Use Generative Adversarial Networks (GANs) to generate images
- Use deep learning for style transfer
- Generate text with RNNs and Natural Language Processing
- Serve Tensorflow Models through an API
- Use GPUs for accelerated deep learning
Requirements
- Know how to code in Python
- Some math basics such as derivatives
Target audience
- Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence
Complete A.I. & Machine Learning, Data Science Bootcamp
★★★★★
$17.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
Created by: Andrei Neagoie
Founder of zerotomastery.io
Founder of zerotomastery.io
Created by: Daniel Bourke
Machine Learning Engineer/Writer/Video maker
Machine Learning Engineer/Writer/Video maker
Rating:4.63 (19893reviews)
112472students enrolled
What Will I Learn?
- Become a Data Scientist and get hired
- Master Machine Learning and use it on the job
- Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
- Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
- Present Data Science projects to management and stakeholders
- Learn which Machine Learning model to choose for each type of problem
- Real life case studies and projects to understand how things are done in the real world
- Learn best practices when it comes to Data Science Workflow
- Implement Machine Learning algorithms
- Learn how to program in Python using the latest Python 3
- How to improve your Machine Learning Models
- Learn to pre process data, clean data, and analyze large data.
- Build a portfolio of work to have on your resume
- Developer Environment setup for Data Science and Machine Learning
- Supervised and Unsupervised Learning
- Machine Learning on Time Series data
- Explore large datasets using data visualization tools like Matplotlib and Seaborn
- Explore large datasets and wrangle data using Pandas
- Learn NumPy and how it is used in Machine Learning
- A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
- Learn to use the popular library Scikit-learn in your projects
- Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
- Learn to perform Classification and Regression modelling
- Learn how to apply Transfer Learning
Requirements
- No prior experience is needed (not even Math and Statistics). We start from the very basics.
- A computer (Linux/Windows/Mac) with internet connection.
- Two paths for those that know programming and those that don't.
- All tools used in this course are free for you to use.
Target audience
- Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python
- You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable
- Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field
- You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry
- You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”
- You want to learn to use Deep learning and Neural Networks with your projects
- You want to add value to your own business or company you work for, by using powerful Machine Learning tools.
Deep Learning Masterclass with TensorFlow 2 Over 20 Projects
★★★★★
$13.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Master Deep Learning with TensorFlow 2 with Computer Vision,Natural Language Processing, Sound Recognition & Deployment
Created by: Neuralearn Dot AI
Helping millions of learners, master Deep Learning.
Helping millions of learners, master Deep Learning.
Rating:4.2 (238reviews)
4024students enrolled
What Will I Learn?
- The Basics of Tensors and Variables with Tensorflow
- Basics of Tensorflow and training neural networks with TensorFlow 2.
- Convolutional Neural Networks applied to Malaria Detection
- Building more advanced Tensorflow models with Functional API, Model Subclassing and Custom Layers
- Evaluating Classification Models using different metrics like: Precision,Recall,Accuracy and F1-score
- Classification Model Evaluation with Confusion Matrix and ROC Curve
- Tensorflow Callbacks, Learning Rate Scheduling and Model Check-pointing
- Mitigating Overfitting and Underfitting with Dropout, Regularization, Data augmentation
- Data augmentation with TensorFlow using TensorFlow image and Keras Layers
- Advanced augmentation strategies like Cutmix and Mixup
- Data augmentation with Albumentations with TensorFlow 2 and PyTorch
- Custom Loss and Metrics in TensorFlow 2
- Eager and Graph Modes in TensorFlow 2
- Custom Training Loops in TensorFlow 2
- Integrating Tensorboard with TensorFlow 2 for data logging, viewing model graphs, hyperparameter tuning and profiling
- Machine Learning Operations (MLOps) with Weights and Biases
- Experiment tracking with Wandb
- Hyperparameter tuning with Wandb
- Dataset versioning with Wandb
- Model versioning with Wandb
- Human emotions detection
- Modern convolutional neural networks(Alexnet, Vggnet, Resnet, Mobilenet, EfficientNet)
- Transfer learning
- Visualizing convnet intermediate layers
- Grad-cam method
- Model ensembling and class imbalance
- Transformers in Vision
- Model deployment
- Conversion from tensorflow to Onnx Model
- Quantization Aware training
- Building API with Fastapi
- Deploying API to the Cloud
- Object detection from scratch with YOLO
- Image Segmentation from scratch with UNET model
- People Counting from scratch with Csrnet
- Digit generation with Variational autoencoders (VAE)
- Face generation with Generative adversarial neural networks (GAN)
- Sentiment Analysis with Recurrent neural networks, Attention Models and Transformers from scratch
- Neural Machine Translation with Recurrent neural networks, Attention Models and Transformers from scratch
- Intent Classification with Deberta in Huggingface transformers
- Neural Machine Translation with T5 in Huggingface transformers
- Extractive Question Answering with Longformer in Huggingface transformers
- E-commerce search engine with Sentence transformers
- Lyrics Generator with GPT2 in Huggingface transformers
- Grammatical Error Correction with T5 in Huggingface transformers
- Elon Musk Bot with BlenderBot in Huggingface transformers
Requirements
- Basic Math
- Access to an internet connection, as we shall be using Google Colab (free version)
- Basic Knowledge of Python
Target audience
- Beginner Python Developers curious about Applying Deep Learning for Computer vision and Natural Language Processing
- Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood
- Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow.
- Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.
- Natural Language Processing practitioners who want to learn how state of art NLP models are built and trained using deep learning.
- Anyone wanting to deploy ML Models
- Learners who want a practical approach to Deep learning for Computer vision, Natural Language Processing and Sound recognition
A deep understanding of deep learning (with Python intro)
★★★★★
$15.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.
Created by: Mike X Cohen
Educator and writer
Educator and writer
Rating:4.73 (3211reviews)
26991students enrolled
What Will I Learn?
- The theory and math underlying deep learning
- How to build artificial neural networks
- Architectures of feedforward and convolutional networks
- Building models in PyTorch
- The calculus and code of gradient descent
- Fine-tuning deep network models
- Learn Python from scratch (no prior coding experience necessary)
- How and why autoencoders work
- How to use transfer learning
- Improving model performance using regularization
- Optimizing weight initializations
- Understand image convolution using predefined and learned kernels
- Whether deep learning models are understandable or mysterious black-boxes!
- Using GPUs for deep learning (much faster than CPUs!)
Requirements
- Interest in learning about deep learning!
- Python/Pytorch skills are taught in the course
- A Google account (google-colab is used as the Python IDE)
Target audience
- Students in a deep learning course
- Machine-learning enthusiasts
- Anyone interested in mechanisms of AI (artificial intelligence)
- Data scientists who want to expand their library of skills
- Aspiring data scientists
- Scientists and researchers interested in deep learning
Mathematical Foundations of Machine Learning
★★★★★
$16.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch
Created by: Dr Jon Krohn
Chief Data Scientist and #1 Bestselling Author
Chief Data Scientist and #1 Bestselling Author
Created by: SuperDataScience Team
Helping Data Scientists Succeed
Helping Data Scientists Succeed
Created by: Ligency Team
Helping Data Scientists Succeed
Helping Data Scientists Succeed
Rating:4.63 (5434reviews)
115598students enrolled
What Will I Learn?
- Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science
- Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
- How to apply all of the essential vector and matrix operations for machine learning and data science
- Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA
- Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion)
- Appreciate how calculus works, from first principles, via interactive code demos in Python
- Intimately understand advanced differentiation rules like the chain rule
- Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch
- Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent
- Use integral calculus to determine the area under any given curve
- Be able to more intimately grasp the details of cutting-edge machine learning papers
- Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning
Requirements
- All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.
- Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics.
Target audience
- You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities
- You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
- You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline
- You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)
Complete Guide to TensorFlow for Deep Learning with Python
★★★★★
$18.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques!
Created by: Jose Portilla
Head of Data Science at Pierian Training
Head of Data Science at Pierian Training
Rating:4.6 (16801reviews)
95656students enrolled
What Will I Learn?
- Understand how Neural Networks Work
- Build your own Neural Network from Scratch with Python
- Use TensorFlow for Classification and Regression Tasks
- Use TensorFlow for Image Classification with Convolutional Neural Networks
- Use TensorFlow for Time Series Analysis with Recurrent Neural Networks
- Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
- Learn how to conduct Reinforcement Learning with OpenAI Gym
- Create Generative Adversarial Networks with TensorFlow
- Become a Deep Learning Guru!
Requirements
- Some knowledge of programming (preferably Python)
- Some basic knowledge of math (mean, standard deviation, etc..)
Target audience
- Python students eager to learn the latest Deep Learning Techniques with TensorFlow
Machine Learning, Data Science and Generative AI with Python
★★★★★
$23.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Complete hands-on machine learning and AI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks
Created by: Sundog Education by Frank Kane
Founder, Sundog Education. Machine Learning Pro
Founder, Sundog Education. Machine Learning Pro
Created by: Frank Kane
Founder, Sundog Education
Founder, Sundog Education
Created by: Sundog Education Team
Sundog Education Team
Sundog Education Team
Rating:4.61 (30241reviews)
187957students enrolled
What Will I Learn?
- Build artificial neural networks with Tensorflow and Keras
- Implement machine learning at massive scale with Apache Spark's MLLib
- Classify images, data, and sentiments using deep learning
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Data Visualization with MatPlotLib and Seaborn
- Understand reinforcement learning - and how to build a Pac-Man bot
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross validation to choose and tune your models
- Build a movie recommender system using item-based and user-based collaborative filtering
- Clean your input data to remove outliers
- Design and evaluate A/B tests using T-Tests and P-Values
Requirements
- You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software.
- Some prior coding or scripting experience is required.
- At least high school level math skills will be required.
Target audience
- Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
- Technologists curious about how deep learning really works
- Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.
- If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.
Deep Learning A-Z 2023: Neural Networks, AI & ChatGPT Prize
★★★★★
$19.00 in stock
Udemy.com
as of July 17, 2024 9:57 am
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.
Created by: Kirill Eremenko
Data Scientist
Data Scientist
Created by: Hadelin de Ponteves
Passionate AI Instructor
Passionate AI Instructor
Created by: SuperDataScience Team
Helping Data Scientists Succeed
Helping Data Scientists Succeed
Created by: Ligency Team
Helping Data Scientists Succeed
Helping Data Scientists Succeed
Rating:4.54 (44896reviews)
371468students enrolled
What Will I Learn?
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice
Requirements
- High school mathematics level
- Basic Python programming knowledge
Target audience
- Anyone interested in Deep Learning
- Students who have at least high school knowledge in math and who want to start learning Deep Learning
- Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning
- Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
- Any students in college who want to start a career in Data Science
- Any data analysts who want to level up in Deep Learning
- Any people who are not satisfied with their job and who want to become a Data Scientist
- Any people who want to create added value to their business by using powerful Deep Learning tools
- Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business
- Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms
Price Statistics for TensorFlow Course
- All prices mentioned above are in United States dollar.
- This product is available at Udemy.
- At udemy.com you can purchase Deep Learning Masterclass with TensorFlow 2 Over 20 Projects for only $13.00
- The lowest price of Tensorflow 2.0: Deep Learning and Artificial Intelligence was obtained on July 17, 2024 9:57 am.
As you stand on the brink of unlocking the vast potential of machine learning, the courses outlined above offer you the keys to the kingdom of TensorFlow. Each course has been designed to not only impart theoretical knowledge but also to provide the practical skills necessary to turn that knowledge into real-world applications. Whether you're looking to understand the basics or tackle the complexities of deep learning, these top TensorFlow courses are your gateway to becoming proficient in one of the most sought-after technologies in today's job market.
