# Getting started with TensorFlow 2 Review (2026): 7.6/10 · Coursera > Independent review of Getting started with TensorFlow 2 on Coursera. Rated 7.6/10 by our editorial team. Pros, cons, price, and top alternatives. Certificate… Machine Learning Courses Getting started with TensorFlow 2 ![Getting started with TensorFlow 2](/api/media/file/hero/getting-started-with-tensor-flow2-course.webp?v=2?width=800) # Getting started with TensorFlow 2 Course — Review (7.6/10) This course delivers a solid introduction to TensorFlow 2 with a strong emphasis on practical coding. Learners appreciate the clear tutorials and immediate application of concepts. Some found the pace... Explore This Course 🎟️ Coursera Discount Offer Explore This Course Getting started with TensorFlow 2 is a 7 weeks online beginner-level course on Coursera by Imperial College London that covers machine learning. This course delivers a solid introduction to TensorFlow 2 with a strong emphasis on practical coding. Learners appreciate the clear tutorials and immediate application of concepts. Some found the pace quick in later modules, and additional math background would enhance understanding. Overall, it's a valuable starting point for aspiring deep learning practitioners. We rate it 7.6/10. ## Prerequisites No prior experience required. This course is designed for complete beginners in machine learning. ## Pros - Excellent hands-on coding tutorials that reinforce learning - Clear explanations of TensorFlow 2 and Keras integration - Practical focus on real-world model development workflow - Good introduction to callbacks, regularization, and model saving ## Cons - Limited theoretical depth on underlying neural network math - Assumes prior Python and basic ML knowledge - Some labs could use more detailed feedback mechanisms ## Getting started with TensorFlow 2 Course Review Platform: Coursera Instructor: Imperial College London Updated May 5, 2026·Editorial Standards·How We Rate ## What will you learn in Getting started with TensorFlow 2 course - Build and train deep learning models using TensorFlow 2 and the Keras Sequential API - Implement model validation and apply regularization techniques to improve generalization - Use callbacks such as early stopping and learning rate scheduling during training - Save and load trained models for deployment and reuse - Make predictions with trained models and evaluate their performance effectively ### Program Overview ### Module 1: Introduction to TensorFlow 2 2 weeks - Introduction to tensors and operations in TensorFlow - Building your first neural network with Keras - Understanding model compilation and training basics ### Module 2: Model Training and Evaluation 2 weeks - Training deep learning models on real datasets - Evaluating model performance using metrics - Splitting data and using validation sets ### Module 3: Improving Model Performance 2 weeks - Implementing regularization techniques like dropout - Using callbacks for efficient training - Monitoring training with TensorBoard ### Module 4: Model Saving and Deployment 1 week - Saving and loading models using HDF5 and SavedModel formats - Making predictions on new data - Best practices for model persistence and reuse ### Get certificate #### Job Outlook - Relevant for roles in machine learning engineering and data science - Builds foundational skills applicable in AI-driven industries - Supports career advancement in deep learning and model development ## Editorial Take This course from Imperial College London serves as a practical gateway into the world of TensorFlow 2, tailored for learners who want to transition quickly from theory to implementation. It emphasizes a hands-on approach, ensuring that foundational concepts in deep learning are reinforced through immediate coding exercises. While it assumes some prior knowledge, it structures the learning path to build confidence in using one of the most popular deep learning frameworks. ### Standout Strengths - Hands-on Coding Approach: Each module integrates practical Jupyter notebook exercises, allowing learners to build models from day one. This immediate application helps solidify abstract concepts through real implementation. - Sequential API Focus: The course effectively leverages Keras' high-level API, making it accessible for beginners. Learners gain confidence by quickly constructing functional models without getting bogged down by low-level details. - Model Lifecycle Coverage: From building to saving models, the course walks through the entire workflow. This end-to-end perspective is rare in introductory courses and prepares learners for real-world development tasks. - Regularization and Validation: The inclusion of dropout, validation splits, and overfitting detection adds practical depth. These techniques are essential for creating models that generalize well beyond training data. - Callback Implementation: Teaching early stopping and learning rate scheduling shows an understanding of efficient training. These tools help learners optimize performance and reduce wasted computation time. - Model Persistence: Covering both HDF5 and SavedModel formats ensures learners can deploy models across environments. This attention to deployment readiness enhances the course's practical value. ### Honest Limitations - Limited Theoretical Depth: While practical, the course offers minimal explanation of gradient descent or backpropagation mechanics. This trade-off speeds up coding but may leave gaps in deeper understanding. - Pacing in Later Modules: Some learners report that Module 3 accelerates quickly, especially when introducing TensorBoard. A bit more scaffolding could improve accessibility for true beginners. ### How to Get the Most Out of It - Study cadence: Dedicate 3–4 hours weekly with consistent scheduling. Spaced practice enhances retention, especially when working through coding notebooks and debugging model issues. - Parallel project: Build a personal image classifier alongside the course. Applying concepts to a unique dataset reinforces learning and builds a portfolio piece. - Note-taking: Document each model architecture and hyperparameter choice. This creates a personal reference guide for future projects and debugging. - Community: Engage with Coursera forums to troubleshoot errors. Many common TensorFlow issues have been solved by prior learners, saving debugging time. - Practice: Re-run notebooks with modified parameters to observe changes in training dynamics. Experimentation deepens understanding of model behavior and convergence. - Consistency: Complete assignments promptly to maintain momentum. Delaying practice risks knowledge decay, especially with syntax-heavy frameworks like TensorFlow. ### Supplementary Resources - Book: 'Deep Learning with Python' by François Chollet complements this course perfectly. It provides deeper context for Keras and TensorFlow patterns introduced here. - Tool: Use Google Colab for free GPU access during labs. This accelerates training and mirrors industry-standard cloud environments. - Follow-up: Enroll in 'TensorFlow in Practice' Specialization to advance to CNNs and transfer learning. This course is an ideal prerequisite. - Reference: TensorFlow’s official documentation offers detailed API references. Bookmark it for quick lookups during and after the course. ### Common Pitfalls - Pitfall: Skipping the math entirely can hinder long-term progress. Even basic understanding of gradients and loss functions improves debugging ability and model design. - Pitfall: Copying code without understanding leads to fragile knowledge. Always modify and experiment with provided examples to test comprehension. - Pitfall: Ignoring version compatibility issues between TensorFlow and Python can cause runtime errors. Always verify environment setup before starting labs. ### Time & Money ROI - Time: At 7 weeks with 3–5 hours weekly, the time investment is reasonable for the skills gained. Completion yields tangible portfolio-ready projects. - Cost-to-value: As a paid course, the value is moderate. Free alternatives exist, but structured guidance and certification justify the fee for career-focused learners. - Certificate: The verified certificate adds credibility to resumes, especially when applying for entry-level ML roles or upskilling within technical teams. - Alternative: Free YouTube tutorials may cover similar content, but lack assessments and structured progression. This course offers accountability and guided learning. ### Editorial Verdict This course successfully bridges the gap between theoretical machine learning and practical implementation using TensorFlow 2. By focusing on the Keras Sequential API, it lowers the entry barrier for beginners while still delivering meaningful, production-relevant skills. The integration of model saving, callbacks, and regularization ensures learners are not just building models, but building them well. The hands-on structure, combined with Imperial College London’s academic rigor, makes this a trustworthy starting point for anyone serious about deep learning. That said, it’s not without trade-offs. The course prioritizes coding over theory, which may leave some learners curious about the 'why' behind the algorithms. Additionally, the assumption of prior Python knowledge means true beginners might need supplementary study. However, for its target audience—those with basic programming experience looking to enter deep learning—it delivers strong value. With a balanced mix of structure and flexibility, this course earns a solid recommendation as a first step in mastering TensorFlow. ## How Getting started with TensorFlow 2 Compares | Course | Platform | Rating | Level | Duration | | --- | --- | --- | --- | --- | | Getting started with TensorFlow 2 | Coursera | 7.6/10 | Beginner | 7 weeks | | Machine Learning, Data Science and Generative AI with Python Course | Udemy | 9.7/10 | N/A | N/A | | Machine Learning with Mahout Certification Training Course | Edureka | 9.7/10 | N/A | N/A | | Introduction to Graph Machine Learning Course | Educative | 9.7/10 | N/A | N/A | ## Who Should Take Getting started with TensorFlow 2? This course is best suited for learners with no prior experience in machine learning. It is designed for career changers, fresh graduates, and self-taught learners looking for a structured introduction. The course is offered by Imperial College London on Coursera, combining institutional credibility with the flexibility of online learning. Upon completion, you will receive a course certificate that you can add to your LinkedIn profile and resume, signaling your verified skills to potential employers. If you are exploring adjacent fields, you might also consider courses in Agile & Scrum Courses, AI Courses, Arts and Humanities Courses, which complement the skills covered in this course. ### Career Outcomes - Apply machine learning skills to real-world projects and job responsibilities - Qualify for entry-level positions in machine learning and related fields - Build a portfolio of skills to present to potential employers - Add a course certificate credential to your LinkedIn and resume - Continue learning with advanced courses and specializations in the field ## More Machine Learning Courses on Coursera Explore other highly rated courses in machine learning available on Coursera to expand your learning path: - Structuring Machine Learning Projects Course 9.8/10 - Machine Learning Specialization Course 9.7/10 - Mathematics for Machine Learning: Multivariate Calculus Course 9.7/10 - Machine Learning in Production Course 9.7/10 - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Course 9.7/10 - Data Science: Statistics and Machine Learning Specialization Course 9.7/10 - Machine Learning with Python Course 9.7/10 - IBM Introduction to Machine Learning Specialization Course 9.7/10 - Practical Machine Learning Course 9.7/10 - Machine Learning: Classification Course 9.7/10 ## Top Alternatives on Other Platforms Looking for a different teaching style or approach? These top-rated machine learning courses from other platforms cover similar ground: - Machine Learning, Data Science and Generative AI with Python Course 9.7/10 Udemy - Machine Learning with Mahout Certification Training Course 9.7/10 Edureka - Introduction to Graph Machine Learning Course 9.7/10 Educative - Cluster Analysis and Unsupervised Machine Learning in Python Course 9.7/10 Udemy - HarvardX: Data Science: Building Machine Learning Models course 9.7/10 EDX - Python for Data Science and Machine Learning course 9.7/10 EDX - Tiny Machine Learning (TinyML) course 9.7/10 EDX - Applied Tiny Machine Learning (TinyML) for Scale course 9.7/10 EDX - Machine Learning for Absolute Beginners – Level 1 Course 9.6/10 Udemy - Introduction to Machine Learning for Data Science Course 9.6/10 Udemy ## More Courses from Imperial College London Imperial College London offers a range of courses across multiple disciplines. If you enjoy their teaching approach, consider these additional offerings: - Mathematics for Machine Learning: Multivariate Calculus Course 9.7/10 - Mathematics for Machine Learning Specialization Course 8.7/10 - Customising your models with TensorFlow 2 8.7/10 - Developing the SIR Model 8.7/10 - Building on the SIR Model 8.7/10 - Advanced Creative Thinking and AI: Tools for Success Course 8.7/10 - Foundations of Public Health Practice: The Public Health Approach Course 8.7/10 - Coaching Skills for Learner-Centred Conversations Course 8.5/10 View all courses from Imperial College London → ## Related Articles & Guides Deepen your understanding with these articles from our editorial team, covering career advice, industry trends, and learning strategies: - Build AI skills with the Google AI Professional Certificate - Python Tutorial: Best Courses to Learn Python in 2026 - CISSP vs CompTIA Security+: Which Cert Should You Pursue? - Coursera Data Analytics Professional Certificate: Worth It in 2026? - Best edX Courses in 2026: Top Picks by Enrollment and Career Value - Best Online Coursera Courses in 2026: What's Actually Worth Your Time - Udemy Online: What the Platform Actually Delivers in 2026 - OKR for Leaders: 7 Best Training Courses Compared (2026) - Generative AI for Marketing with Microsoft 365 Copilot: Professional Certificate Review - The Best React Courses in 2026, Ranked and Reviewed ## Explore All Course Categories Not sure what to learn next? Browse our full catalog of course categories to find the right fit for your career goals: Agile & Scrum Courses AI Courses Arts and Humanities Courses Business & Management Courses Cloud Computing Courses Computer Science Courses Construction Management Courses Cybersecurity Courses Data Analyst Courses Data Analytics Courses Data Engineering Courses Data Science Courses Design Courses Developer Courses Economics & Finance Courses Education & Teacher Training Courses Entrepreneurship Courses Excel Courses Finance Courses Game Development Courses Graphic Design Courses Health Science Courses Information Technology Courses Language Learning Courses Leadership Courses Lifestyle Courses Machine Learning Courses Marketing Courses Math and Logic Courses Music Courses Negotiation Courses Office Productivity Courses Other Personal Development Courses Photography & Videography Courses Physical Science and Engineering Courses Project Management Courses Python Courses SEO Courses Social Media Marketing Courses Social Sciences Courses Software Development Courses Supply Chain Management Courses Teaching Courses Uncategorized UX Design Courses Web Development Courses Explore related topics Data Science Python Data Engineering Computer Science Explore Related Topics Best Machine Learning Courses Learning Path Best ML & Data Science Courses ML Engineer Career Path Browse All Courses ## User Reviews No reviews yet. Be the first to share your experience! ## FAQs What are the prerequisites for Getting started with TensorFlow 2? No prior experience is required. Getting started with TensorFlow 2 is designed for complete beginners who want to build a solid foundation in Machine Learning. It starts from the fundamentals and gradually introduces more advanced concepts, making it accessible for career changers, students, and self-taught learners. Does Getting started with TensorFlow 2 offer a certificate upon completion? Yes, upon successful completion you receive a course certificate from Imperial College London. This credential can be added to your LinkedIn profile and resume, demonstrating verified skills to employers. In competitive job markets, having a recognized certificate in Machine Learning can help differentiate your application and signal your commitment to professional development. How long does it take to complete Getting started with TensorFlow 2? The course takes approximately 7 weeks to complete. It is offered as a free to audit course on Coursera, which means you can learn at your own pace and fit it around your schedule. The content is delivered in English and includes a mix of instructional material, practical exercises, and assessments to reinforce your understanding. Most learners find that dedicating a few hours per week allows them to complete the course comfortably. What are the main strengths and limitations of Getting started with TensorFlow 2? Getting started with TensorFlow 2 is rated 7.6/10 on our platform. Key strengths include: excellent hands-on coding tutorials that reinforce learning; clear explanations of tensorflow 2 and keras integration; practical focus on real-world model development workflow. Some limitations to consider: limited theoretical depth on underlying neural network math; assumes prior python and basic ml knowledge. Overall, it provides a strong learning experience for anyone looking to build skills in Machine Learning. How will Getting started with TensorFlow 2 help my career? Completing Getting started with TensorFlow 2 equips you with practical Machine Learning skills that employers actively seek. The course is developed by Imperial College London, whose name carries weight in the industry. The skills covered are applicable to roles across multiple industries, from technology companies to consulting firms and startups. Whether you are looking to transition into a new role, earn a promotion in your current position, or simply broaden your professional skillset, the knowledge gained from this course provides a tangible competitive advantage in the job market. Where can I take Getting started with TensorFlow 2 and how do I access it? Getting started with TensorFlow 2 is available on Coursera, one of the leading online learning platforms. You can access the course material from any device with an internet connection — desktop, tablet, or mobile. The course is free to audit, giving you the flexibility to learn at a pace that suits your schedule. All you need is to create an account on Coursera and enroll in the course to get started. How does Getting started with TensorFlow 2 compare to other Machine Learning courses? Getting started with TensorFlow 2 is rated 7.6/10 on our platform, placing it as a solid choice among machine learning courses. Its standout strengths — excellent hands-on coding tutorials that reinforce learning — set it apart from alternatives. What differentiates each course is its teaching approach, depth of coverage, and the credentials of the instructor or institution behind it. We recommend comparing the syllabus, student reviews, and certificate value before deciding. What language is Getting started with TensorFlow 2 taught in? Getting started with TensorFlow 2 is taught in English. Many online courses on Coursera also offer auto-generated subtitles or community-contributed translations in other languages, making the content accessible to non-native speakers. The course material is designed to be clear and accessible regardless of your language background, with visual aids and practical demonstrations supplementing the spoken instruction. Is Getting started with TensorFlow 2 kept up to date? Online courses on Coursera are periodically updated by their instructors to reflect industry changes and new best practices. Imperial College London has a track record of maintaining their course content to stay relevant. We recommend checking the "last updated" date on the enrollment page. Our own review was last verified recently, and we re-evaluate courses when significant updates are made to ensure our rating remains accurate. Can I take Getting started with TensorFlow 2 as part of a team or organization? Yes, Coursera offers team and enterprise plans that allow organizations to enroll multiple employees in courses like Getting started with TensorFlow 2. Team plans often include progress tracking, dedicated support, and volume discounts. This makes it an effective option for corporate training programs, upskilling initiatives, or academic cohorts looking to build machine learning capabilities across a group. What will I be able to do after completing Getting started with TensorFlow 2? After completing Getting started with TensorFlow 2, you will have practical skills in machine learning that you can apply to real projects and job responsibilities. You will be prepared to pursue more advanced courses or specializations in the field. Your course certificate credential can be shared on LinkedIn and added to your resume to demonstrate your verified competence to employers. ## Similar Courses Other courses in Machine Learning Courses ![Getting Started with Google Kubernetes Engine Course](/api/media/file/hero/getting-started-with-google-kubernetes-engine-course.webp?width=480) Coursera Cloud Computing Courses ### Getting Started with Google Kubernetes Engine Course ★★★★½ Coursera View Course » Enroll ![Getting started with Google Workspace Specialization Course](/api/media/file/images/2025/07/Getting-started-with-Google-Workspace-Specialization.webp?width=480) Coursera Information Technology Courses ### Getting started with Google Workspace Specialization Course ★★★★½ Coursera View Course » Enroll ![Getting Started with Go Course](/api/media/file/hero/getting-started-with-go-course.webp?width=480) Coursera Computer Science Courses ### Getting Started with Go Course ★★★★½ Coursera View Course » Enroll ![Getting Started with Microsoft Excel Course](/api/media/file/images/2025/05/Getting-Started-with-Microsoft-Excel-1.webp?width=480) Coursera Excel Courses ### Getting Started with Microsoft Excel Course ★★★★½ Coursera View Course » Enroll ![Getting Started with Mobile App Development with React Native Course](/api/media/file/images/2025/07/Getting-Started-with-Mobile-App-Development-with-React-Native.webp?width=480) Educative Computer Science Courses ### Getting Started with Mobile App Development with React Native Course ★★★★½ Educative View Course » Enroll ![Acoustic Guitar and Electric Guitar Lessons: Getting Started Course](/api/media/file/images/2025/05/Acoustic-Guitar-and-Electric-Guitar-Lessons-Getting-Started.webp?width=480) Udemy Music Courses ### Acoustic Guitar and Electric Guitar Lessons: Getting Started Course ★★★★½ Udemy View Course » Enroll ## Related Job Opportunities ### High School Teacher Asian College Of Teachers is a trading brand of TTA Training Pvt. Ltd Warszawa, PL Full-Time PLN 54–86/yr ### Alternance chargé(e) de communication & marketing produit SaaS - Paris (F/H) OKTOGONE Paris, FR Full-Time ### Bautechnik Freileitungsmast Planung Infrastruktur (m/w/d) 50Hertz Transmission GmbH Berlin, DE Full-Time ### Ingenieur Energietechnik als Projektmanager Inbetriebnahme & Dokumentation (m/w/d) 50Hertz Transmission GmbH Berlin, DE Full-Time ### IT Governance Compliance Managerin (m/w/d) 50Hertz Transmission GmbH Berlin, DE Full-Time Browse more jobs on JobsNearMe.career → ### Explore Related Categories All Machine Learning Courses Explore Course Reviews ### Review: Getting started with TensorFlow 2 Your Name * Email (optional, not displayed) Rating * Your Review * ### Discover More Course Categories Explore expert-reviewed courses across every field Data Science Courses AI Courses Python Courses Web Development Courses Cybersecurity Courses Data Analyst Courses Excel Courses Cloud & DevOps Courses UX Design Courses Project Management Courses SEO Courses Agile & Scrum Courses Business Courses Marketing Courses Software Dev Courses Browse all 10,000+ courses »