1. Module 1: AI Fundamentals and Google AI Overview (Week 1)
- •What AI, ML, and deep learning mean in practice
- •Types of AI: narrow AI, general AI, and generative AI
- •Google AI ecosystem overview
- •Real-world AI applications
- •AI lifecycle overview
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-Recruiters prefer to hire Inventateq Students - Join Inventateq NowPlacements in Google AI: 1,342
Google AI Course Online with Certification
Learn Google AI, Google Cloud AI, Vertex AI, Gemini AI, TensorFlow, and Keras in a structured online format built around hands-on practice. The course takes you from Python and machine learning basics to cloud deployment, AI APIs, and real-time project work.
4.7/5 from 1,432 reviews
Covers Google AI tools used in real projects, not just theory.
You work with Python, NumPy, Pandas, Matplotlib, and Seaborn from the start.
Vertex AI and Gemini API training are built into the syllabus.
TensorFlow and Keras sessions move into model building and validation.
Covers deployment, monitoring, and basic MLOps on Google Cloud.
Ends with chatbot, image classification, sentiment analysis, and recommendation projects.
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Placement Assistance for Google AI Course Online
Learning AI is only part of the outcome. Students also need a clear path from course work to interviews, especially for roles like AI Engineer, ML Engineer, and Generative AI Developer. Inventateq keeps that part practical with resume support, interview practice, and guidance around the projects you build during training.
Support starts while the course is still running. As you finish modules and projects, the team helps you shape those outputs into a portfolio, prepare role-focused answers, and get ready for recruiter conversations with Google AI, ML, and cloud-focused openings.
Our Signature Career Support:
- Resume support built around your AI projects and tools used in class.
- Mock interviews for AI Engineer, ML Engineer, and Generative AI Developer roles.
- Portfolio guidance using chatbot, image classification, and sentiment analysis work.
- Interview preparation tied to Python, TensorFlow, Vertex AI, and Gemini APIs.
- Career mentoring on how to present cloud deployment and MLOps basics to recruiters.
Google AI Salary Insights
Google AI, machine learning, and cloud AI roles are hiring across product companies, IT services, analytics teams, and cloud-focused startups. Pay grows with how well you can build models, deploy them, and explain the business use case behind the work.
Google AI Average Salary by Experience
Google AI Salary Insights
Google AI, machine learning, and cloud AI roles are hiring across product companies, IT services, analytics teams, and cloud-focused startups. Pay grows with how well you can build models, deploy them, and explain the business use case behind the work.
Google AI Average Salary by Experience
Why Students Choose Our Google AI Online Course?
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About Inventateq
Inventateq has built a steady training environment for learners who want structured technical classes, guided practice, and project-based learning. For a Google AI course online, that matters because students need more than video access; they need real instruction, feedback, and a place to finish the work they start.
We stand apart through our commitment to:
- Years of training experience across technical courses and job-focused programs.
- A classroom-style teaching approach that keeps sessions structured and accountable.
- Curriculum updates are aligned with tools people actually use, such as Python, TensorFlow, Vertex AI, and Gemini APIs.
- Learners from different backgrounds have relied on Inventateq for practical upskilling.
- Dedicated support for projects, interview prep, and course completion.
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Attend live, instructor-led classes from anywhere with the same hands-on structure as our classroom batches. Follow along step-by-step, get real-time doubt support, and revisit recordings whenever you need to.
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Dedicated Doubt-Solving Sessions with Mentors
Study Guides, PPTs, and Exam Guidance Included
Class Recordings and Backup Sessions for Missed Classes
Flexible Weekday and Weekend Batch Timings
Career Guidance and Interview Preparation Support
Details of Inventateq Google AI Course
Fresh graduates
Good for learners who want a job-ready entry into AI, ML, and cloud AI tools.
Software developers
Useful for developers who want to move into AI-powered apps, APIs, and model deployment.
Data professionals
Fits analysts and data workers who want stronger machine learning and deep learning skills.
Cloud learners
Helps people who already know GCP basics and want to work with Vertex AI and Gemini APIs.
Career switchers
Works for motivated learners ready to build Python and AI fundamentals from the ground up.
Project-focused students
Ideal for anyone who wants practical chatbot, classification, and recommendation projects.
Quick Highlights of Inventateq Google AI Course
Built as a step-by-step online program with mentor-led sessions and project work.
Structured format: Topics move from AI basics to cloud deployment in a clear sequence.
Live classes: Sessions are instructor-led, not self-paced video dumping.
Project phase: Real-Time Projects comes after the core modules.
Interview prep: Resume building and interview preparation are included near the end.
Google AI Course Curriculum
W1
2. Module 2: Google AI Tools and Development Environment (Week 2)
- •Google AI and Google Cloud AI overview
- •Introduction to Vertex AI
- •Google Colab environment for model work
- •TensorFlow ecosystem basics
- •Gemini AI overview
3. Module 3: Python for AI Development (Week 3)
- •Python basics for AI development
- •NumPy and Pandas for data handling
- •Data cleaning and preprocessing
- •Data visualization with Matplotlib and Seaborn
- •Working with datasets
4. Module 4: Machine Learning Basics (Week 4)
- •Supervised and unsupervised learning
- •Regression and classification models
- •Clustering techniques
- •Model training workflow
- •Evaluation metrics
5. Module 5: Deep Learning Fundamentals (Week 5)
- •Neural network basics
- •Activation functions
- •Backpropagation concept
- •CNN concepts for image tasks
- •RNN concepts for sequence tasks
6. Module 6: TensorFlow and Keras (Week 6)
- •Introduction to TensorFlow
- •Keras API basics
- •Building neural network models
- •Model training and validation
- •Saving and loading models
7. Module 7: Generative AI with Google Focus (Week 7)
- •Introduction to generative AI
- •Large language models
- •Prompt engineering basics
- •Gemini API overview
- •Text, image, and multimodal AI concepts
8. Module 8: Vertex AI and Cloud AI Services (Week 8)
- •Vertex AI platform overview
- •AutoML concepts
- •Model deployment on cloud
- •Pipelines and MLOps basics
- •Model monitoring
9. Module 9: AI APIs and Integration (Week 9)
- •Google AI APIs for vision, speech, and NLP
- •REST API integration
- •AI-powered application development
- •Chatbot development
- •Real-time AI applications
10. Module 10: MLOps and Deployment (Week 10)
- •CI/CD for ML models
- •Docker basics for AI apps
- •Cloud deployment strategies
- •Model versioning
- •Scalability concepts
11. Module 11: Real-Time Projects and Interview Preparation (Week 11)
- •AI chatbot using Gemini API
- •Image classification system
- •Sentiment analysis project
- •Recommendation system
- •Resume building and interview preparation
Student Reviews – Google AI
4.7 Star Rating from 1,432+ Google Reviews
Rated 4.9/5 by AI Students
Why Learn Google AI Today?
Teams are already using AI in search, support, analytics, content generation, and product automation. Learning Google AI now gives you a path into tools that are being adopted for building, deploying, and monitoring practical AI systems.
Why Students Trust Inventateq for Google AI Training
- The syllabus connects core AI concepts with Google tools, so learners see the full path from theory to application.
- Vertex AI, Gemini APIs, and TensorFlow are taught in a way that fits current cloud-first AI work.
- The course does not stop at model building; deployment, monitoring, and versioning are part of the training.
- Real-time projects make it easier to show practical output when applying for AI roles.
- Inventateq's mentor-led format helps learners keep pace with technical topics that are hard to learn alone.
Build Practical Google AI Skills That Can Be Used at Work
By the end of the course, learners should be able to move from data prep to model training and then into cloud-based AI application work. The focus is on doing the steps, not just naming them.
Prepare data for AI models
Work with Python, NumPy, and Pandas to clean, organize, and inspect datasets before training starts. You will know how to handle basic preprocessing and visual checks with Matplotlib and Seaborn.
Train machine learning models
Set up supervised and unsupervised learning workflows, then work through regression, classification, and clustering. The course shows how model training and evaluation fit together.
Build deep learning networks
Create neural network models with TensorFlow and Keras and understand how activation functions and backpropagation support learning. You also see where CNNs and RNNs are used.
Use Google AI tools in projects
Apply Vertex AI, Google Colab, Gemini API, and other Google AI services in guided exercises. This is where the course shifts from theory to tool-based implementation.
Develop and deploy AI applications
Connect AI APIs, build chatbot-style features, and understand cloud deployment, model versioning, and monitoring. The deployment part gives the training a production angle.
Present interview-ready project work
Finish with chatbot, image classification, sentiment analysis, and recommendation projects that can be explained in interviews. The course also includes resume building and interview preparation.
Detailed Insights :: Google AI Training Online
Students Most Asked Questions
Is this Google AI course beginner-friendly?
Yes, the syllabus starts with AI concepts and Python basics before moving into machine learning, deep learning, and cloud AI tools. That sequence gives new learners a clear entry point instead of dropping them into advanced topics first.
Will I get hands-on project experience?
You will work on projects in the final module, including a chatbot, image classification, sentiment analysis, and recommendation system. Those projects are important because they turn the class material into something you can show and explain.
Does Inventateq help with placement support?
The course includes resume building and interview preparation, along with support that connects your projects to role-specific discussions. That makes the job search side more practical, especially for AI and ML roles.
Can someone from a non-technical background join?
Yes, if the learner is ready to spend time on Python and basic data handling early in the course. The first modules are built to introduce the core concepts before the training moves into models, APIs, and deployment.
Is the course available online?
Yes, this page is built for online learning from any location. The live format is meant to keep the course interactive while still letting you learn remotely.
How long does the syllabus run?
The curriculum is organized into 11 modules, moving from fundamentals to real-time projects and interview preparation. That structure gives enough room to cover both concept work and practical implementation.
Which tool matters most in the course: TensorFlow, Vertex AI, or Gemini APIs?
They all matter, but for different reasons. TensorFlow and Keras handle model building, Vertex AI handles the cloud side, and Gemini APIs support generative AI and chatbot work. The course is designed so you see how those pieces fit together instead of learning them in isolation.
Explore Our Training Locations
Inventateq offers classroom training across multiple locations. Explore the branch nearest to you and check available batch timings.
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