AI Powered Course
AI Engineering Intermediate Online Training
Start your AI journey with industry-ready skills
Program Highlights
As AI moves from experimentation to real-world adoption, the ability to build practical, scalable AI solutions is becoming a critical skill. InfosecTrain’s AI Engineering Intermediate Training bridges the gap between basic AI understanding and hands-on model development, guiding learners deeper into applied machine learning and neural network workflows. Through structured practice with real datasets, Python-based model building, and exploration of Generative AI tools, participants strengthen their technical abilities and gain confidence in executing full AI pipelines. This AI engineering course will empower learners to handle complex data, compare models, create simple neural networks, and complete a guided mini project that reflects practical, industry-level AI problem-solving.
22 Hours LIVE Instructor-led Training 
Hands-on Exercises 
Real-world Applications 
Practical Frameworks 
Case Studies 
Certified Experts 
Career Guidance & Mentorship 
Dedicated Telegram Support Group 
Access to Recorded Sessions
Training Schedule
- upcoming classes
- corporate training
- 1 on 1 training
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About Course
The AI Engineering Intermediate Training by InfosecTrain will strengthen practical skills in building real-world AI and machine learning solutions. It is designed for learners who have completed foundations or possess basic Python knowledge. This AI engineering course online focuses on hands-on implementation, real datasets, neural networks, and emerging generative AI tools. Through guided labs, industry use cases, and a structured mini project, participants build confidence in executing end-to-end AI pipelines and workflows, preparing for advanced specialization and applied AI roles.
Course Curriculum
- Module 1: Strengthening Python for AI Projects
- Refresh essential Python concepts for AI: functions, loops, and error handling, but with a stronger
focus on writing clean, reusable, production-quality code. - Work hands-on with NumPy and Pandas to process real-world datasets efficiently.
- Visualize patterns and insights using Matplotlib and Seaborn for storytelling with data.
- Learn the basics of Git and GitHub workflows, version control, branching, and collaborative coding.
- Mini-lab: Fix a broken AI script, track changes with Git, and push your first AI project to GitHub.
- Refresh essential Python concepts for AI: functions, loops, and error handling, but with a stronger
- Module 2: Advanced Data Handling & Preparation
- Tackle messy, real-world datasets: missing values, duplicates, outliers, and inconsistent formats
- Learn feature engineering techniques to extract hidden patterns and improve accuracy.
- Handle imbalanced datasets using oversampling, undersampling, and SMOTE.
- Explore data augmentation for text and images to boost small datasets.
- Automate workflows with scikit-learn Pipelines, ensuring smooth transitions from raw data to model-ready
input. - Hands-on: Clean and prepare a financial fraud dataset, engineering features to boost model performance.
- Module 3: Building & Comparing Machine Learning Models
- Refresh classical models: Logistic Regression, Linear Regression, Decision Trees, Random Forests.
- Step up to Gradient Boosting methods (XGBoost, LightGBM, CatBoost), the models behind many Kaggle wins
and enterprise AI solutions. - Evaluate performance with accuracy, precision, recall, F1-score, confusion matrix, ROC/AUC, and learn
when each matters most. - Hands-on lab: Train multiple models on the same dataset (e.g., customer churn). Compare results and
justify model choice with metrics. - Gain confidence in selecting the best model for the business problem, not just the one with the highest
accuracy.
- Module 4: Neural Networks & Generative AI
- Part A: Neural Networks from Scratch
- Demystify neural networks with visual intuition: layers, weights, activation functions, and
backpropagation. - Build a simple neural network with TensorFlow/Keras to classify handwritten digits.
- Learn the impact of different activation functions (ReLU, Sigmoid, Softmax) on model behavior.
- Hands-on: Train and test your own neural net, tweak hyperparameters, and watch how performance
changes.
- Demystify neural networks with visual intuition: layers, weights, activation functions, and
- Part B: Introduction to Generative AI
- Understand how transformers and embeddings power modern AI.
- Work with pre-trained Hugging Face models for text summarization and classification.
- Experiment with GPT-style models for text generation and Stable Diffusion for creative AI.
- Hands-on: Build a text summarizer app using Hugging Face pipelines, or generate images with
prompts.
- Part A: Neural Networks from Scratch
- Module 5: Guided Mini Projects & Wrap-Up
- Apply everything learned in a structured, guided project:
- Predictive AI option: Build a fraud detection or churn prediction model with structured data.
- Generative AI option: Create a text summarizer or chatbot using a pre-trained LLM.
- Complete the end-to-end pipeline: data preparation → model training → evaluation
- simple deployment.
- Wrap-up session: roadmap for advancing to Pro level (deployment pipelines, MLOps, agentic AI).
- Apply everything learned in a structured, guided project:
Target Audience
This training is ideal for:
- Learners who completed Foundations or have basic Python/AI knowledge
Pre-requisites
- Completion of AI Engineering Foundations or basic Python/AI knowledge
- Basic understanding of programming and working with data
Course Objectives
Upon successful completion of the training, participants will be able to:
- Improve Python coding skills for AI projects
- Clean and prepare real-world datasets
- Build and evaluate machine learning models
- Understand and create simple neural networks
- Explore Generative AI tools for creating apps
- Complete a mini AI project to apply all skills
Vision
Goal
Skill-Building
Mentoring
Direction
Support
Success
It was a very good experience with the team. The class was clear and understandable, and it benefited me in learning all the concepts and gaining valuable knowledge.
I loved the overall training! Trainer is very knowledgeable, had clear understanding of all the topics covered. Loved the way he pays attention to details.
I had a great experience with the team. The training advisor was very supportive, and the trainer explained the concepts clearly and effectively. The program was well-structured and has definitely enhanced my skills in AI. Thank you for a wonderful learning experience.
The class was really good. The instructor gave us confidence and delivered the content in an impactful and easy-to-understand manner.
The program helped me understand several areas I was unfamiliar with. The instructor was exceptionally skilled and confident in delivering content.
The program was well-structured and easy to follow. The instructor’s use of real-life AI examples made it easier to connect with and understand the concepts.
Frequently Asked Questions
What is the AI Engineering Intermediate training course offered by InfosecTrain?
The AI Engineering Intermediate program is an applied, hands-on learning experience designed to build practical skills in coding AI models, working with real-world datasets, and exploring neural networks and Generative AI. This intermediate AI engineering course strengthens real implementation ability beyond theoretical learning.
Who should join the AI Engineering Intermediate online course?
This course is ideal for learners who have completed foundations or anyone with basic Python and AI understanding, looking to advance into applied AI engineering and real project-based learning.
What advanced skills are covered in the AI Engineering Intermediate program?
Learners develop skills in model building, evaluation, feature engineering, neural networks, Generative AI, and AI pipelines and automation, along with version control and workflow structuring.
Does this intermediate AI engineering course include hands-on labs and projects?
Yes. The program includes multiple guided labs, real dataset practice, and a mini project, enabling practical application of concepts in real-world AI scenarios.
What are the prerequisites for the AI Engineering Intermediate training?
Basic understanding of Python and AI concepts, or completion of InfosecTrain’s Foundations program, is recommended.
How does this course enhance my AI model development and optimization skills?
Learners build and compare machine learning models using metrics like accuracy, precision, recall, F1-score, and ROC/AUC, making it a strong AI model optimization course.
What topics are included in the AI pipelines and MLOps fundamentals module?
The course introduces AI pipelines and workflows, automation using Scikit-learn pipelines, and MLOps fundamentals training, including version control via Git/GitHub.
How is the AI Engineering Intermediate course delivered online?
The course is delivered through AI engineering training online with live instructor-led sessions, interactive labs, and guided project support.
Does this AI training include real-world case studies and industry examples?
Yes, participants work on datasets from industries such as finance, healthcare, and customer analytics to understand real AI problem-solving.
What tools, frameworks, and platforms are taught in this intermediate AI engineering course?
Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow/Keras, Hugging Face, Git/GitHub, and practical Generative AI tools.
Is the AI Engineering Intermediate course suitable for learners transitioning from beginner to advanced AI roles?
Yes. It acts as the ideal bridge between foundational knowledge and advanced specialization, preparing learners for real project-based roles.
Will I receive a course completion certificate from InfosecTrain?
Yes. After finishing the program, you will receive a certificate of completion issued by InfosecTrain.
How does this intermediate course prepare learners for AI engineering job roles?
The course builds confidence in solving real AI tasks, managing datasets, selecting appropriate models, and building complete workflows, essential skills for AI engineering job readiness.
Does the training include AI model deployment and automation techniques?
The course introduces deployment fundamentals and automating AI advanced workflows through pipelines and structured processes.
How does this course help improve my real-world AI problem-solving abilities?
By applying skills on real datasets and completing an end-to-end AI project, learners develop strong analytical and implementation capability for practical environments.
How can I enroll in the AI Engineering Intermediate training with InfosecTrain?
Enrollment can be completed through InfosecTrain’s registration channels or assistance from their training support team.