AI/ML Testing & Quality Engineering Masterclass
(Build strong foundations in Python, ML, NLP, Deep Learning, and GenAI testing methodologies. Equip yourself to analyze model performance, detect drift, and ensure AI reliability.)
AI/ML Testing & Quality Engineering Masterclass is a comprehensive, industry-oriented program designed to transform learners into highly skilled AI Quality Engineers. This course covers the complete spectrum of AI testing—from foundational Python, statistics, and mathematics to advanced Machine Learning, NLP, Deep Learning, and Generative AI validation techniques.
Learners gain hands-on expertise in testing ML models, evaluating data pipelines, validating NLP workflows, assessing LangChain and RAG systems, and ensuring the reliability of modern AI applications. You’ll master critical concepts like feature engineering, ML metrics, model drift detection, embedding evaluation, hallucination testing, and more.
With real-world datasets, project-driven learning, and a capstone focused on AI-driven test case generation & execution validation, the program ensures you become job-ready for one of the fastest-growing roles in tech: AI/ML Testing & Quality Engineering.
Additionally, the practical skills and hands-on experience you gain in this course will significantly boost your confidence and help you excel in interviews.
This course is ideal for testers, QA professionals, automation engineers, developers, and anyone looking to upskill in AI testing and enter the future-ready world of intelligent system validation.
About the Instructor:
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Jatin, our lead trainer for the AI & Machine Learning Testing Master Program, is a highly experienced AI Testing Specialist with a strong background in Machine Learning, NLP, Deep Learning, and Generative AI validation. With a proven track record in the industry, he has been instrumental in helping QA professionals, automation engineers, and developers transition into the fast-growing field of AI Quality Engineering. He has successfully completed 15+ batches and has trained over 200+ learners, many of whom now work in top IT companies across AI, ML, and Automation domains. His teaching style is practical, hands-on, and tailored for real-world use cases—ensuring learners gain not just theoretical clarity but job-ready skills. Jatin’s professional insights, coupled with his deep technical expertise, make him one of the most trusted names in AI Testing education. With his focused interview preparation guidance and real-world project exposure, learners also gain the confidence and skills needed to crack interviews in AI/ML Testing with ease. |
Live Sessions Price:
For LIVE sessions – Offer price after discount is 300 USD 259 119 USD Or USD13000 INR 12900 INR 9900 Rupees
OR
Free Day 3 On:
Indian Timings: 18th December @ 9 PM – 10 PM (IST)/
U.S Timings: 18th December @ 10:30 AM – 11:30 AM (EST)/
U.K Timings: 18th December@ 3:30 PM – 4:30 PM (BST)
Class Schedule:
For Participants in India: Monday to Friday @ 9:00 PM – 10:00 PM (IST)
For Participants in the US: Monday to Friday @ 10:30 AM – 11:30 PM (EST)
For Participants in the UK: Monday to Friday @ 3:30 PM – 4:30 PM (BST)
What students have to say about Jatin:
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👨 Sandeep Verma: 👩 Aishwarya Pillai: 👨 Karthik Ramesh: 👩 Megha Srinivasan: 👨 Deepak Nair: |
Salient Features:
- 50+ Hours of Live Training along with recorded videos
- Lifetime access to the recorded videos
- Course Completion Certificate
Who can enroll for this course?
- Any Testers (Manual, Automation, API, ETL, or Performance Testers) who want to upgrade their skills into AI, ML, NLP, and GenAI testing.
- Automation Test Engineers – looking to integrate AI-driven validation and GenAI tools into automation.
- Developers / SDETs – interested in understanding how to test, evaluate, and monitor ML models.
- Performance Testers & DevOps Engineers – exploring MLOps, model monitoring, and CI/CD-based AI validation.
- Students & Tech Enthusiasts – curious to learn how testing is evolving with AI and Generative AI systems.
- Project Managers & Team Leads – who want to understand AI testing challenges and ensure high-quality AI delivery.
What will I learn by the end of this course?
- Understand Python, statistics, and mathematics essential for AI/ML testing.
- Evaluate ML models using metrics like accuracy, precision, recall, F1-score, RMSE, and R².
- Validate data quality, feature engineering, and preprocessing pipelines.
- Test Machine Learning algorithms, including regression, classification, clustering, and decision trees.
- Perform NLP model testing with tokenization, TF-IDF, Word2Vec, and text evaluation techniques.
- Analyze and test Generative AI workflows using LangChain, RAG, embeddings, and vector stores.
- Validate Deep Learning models, such as ANN, CNN, RNN, and transformers.
- Detect model drift, fairness issues, bias, and reliability gaps.
- Integrate AI testing into automation frameworks and CI/CD pipelines.
- Build and execute real-world AI testing projects, including AI-driven test case generation & model evaluation.
Course syllabus:
Module 1. Python for AI -Testing:
Chapter 1. Pandas
- Introduction to Pandas
- Importing data using read_csv()
- Important functions and attributes
- Masking using Boolean series
- value_count() ,sort_value(), drop duplicate(),Group_by(),isin() function
- The plot function
- The series operation
- The merge function
- Other important functions: set_index(), reset_index(), fillna(), dropna() etc.
- working with the pandas on a real dataset.
Chapter 2. Matplotlib
- Introduction to Matplotlib.
- Basic Plotting (Core Foundation)
- plt.plot() for line plots
- plt.scatter() for scatter plots
- plt.bar() / plt.barh() for bar charts
- plt.hist() for histograms
- plt.boxplot() for distribution analysis
- Figure & Axes
- Labels & Titles
Chapter 3. Data Visualization using Seaborn
- Introduction to Seaborn.
- Relational/statistical vs. scatter plot
- Relational/statistical plot
- Scatter plot
- Line plot
- FacedGrids
Chapter 4 – Plotting with Categorical Data — catplot (Figure Level Function):Categorical Scatterplots
- Stripplot
- Swarmplot
Chapter 5 – Categorical Distribution Plots
- Boxplot
- Violinplot
Chapter 6 – Categorical Estimate Plots
- Pointplot
- Barplot
- Countplot
Chapter 7 – Categorical–Categorical Plots
- Heatmaps
- Clustermaps
Chapter 8 – Plotting Side-by-Side Graphs using FacetGrids
Module 2. Mathematics for AI-Testing
Chapter 1. Role of Mathematics in ML.
- Role of Statistics
- Role of Calculus
- Role of Linear Algebra
- Role of Probability
Chapter 2. Statistics for ML.
- Descriptive Vs. Inferential Statistics.
- Types of data- Numerical Vs. Categorical data
- Studying types of data with graphs
- Univariate Vs. Multivariate analysis
- Descriptive stats visualization
- Working with Categorical data
- Freq. distribution table
- Bar chart
- Pie Chart
- Cumulative freq.
- Working with Numerical data
- Histograms
- BOX Plot
- KDE(kernel density estimation)
- Scatter plot
- Working with Categorical data
- Descriptive stats
- Measure of central tendency
- Measure of spread
- Cumulative distribution function
- Probability density function
Chapter 3. Linear algebra
- Vectors
- what are vectors?
- Type of vectors.
- Geometric meaning of it.
- Real world analysis of Vectors.
- Matrices
- what are matrices
- Type of matrix
- Matrix operations
- Dot product .
- Geometric meaning of it
- Real world analysis of matrix.
- Equation of line, plane and Hyper plane
- Different Forms of equations
- Analysis of the equation in different dimensional space.
- Application of the equations to solve an ML problem.
- Linear combinations, spans and basis vectors
Chapter 4. Probability basics
- Introduction to probability distribution.
- Uniform distribution
- Discrete distribution
- Binomial distribution
- Bernoulli and poisson distribution
- Naive Bayes
- Conditional probability
- Independent event
- Mutually exclusive events
- Bayes theorem
Module 3. Machine Learning Fundamentals
Chapter 1. Basics of ML.
- What is ML?
- Definition and types; helps design testing strategies for predictive systems.
- AI vs ML vs DL – Conceptual differences to pick validation methods aligned with model complexity.
- Types of ML – each requires unique testing approaches (labels, evaluation metrics, reward testing).
- Supervised
- Unsupervised
- Semi supervised
- Reinforcement
- Batch Vs. Online learning.
- Instance based Vs. model based
Chapter 2. Challenges in ML
Chapter 3. Applications of ML in real life – Healthcare, finance, devops: understand domain- specific testing risks.
Chapter 4.Machine Learning Development Life Cycle (MLDLC)
- Data collection ®
- preprocessing ®
- Exploratory data analysis
- Feature engineering
- training and evaluation
- deployment and monitoring; testers check each stage.
Module 4. Basics of Feature Engineering
Chapter 1.What is Feature Engineering?
Chapter 2.Feature Scaling – Standardization(Z-score)
Chapter 3-Feature Scaling – Normalization(Min-Max)
Chapter 4-Encoding Categorical Data – Label and One-Hot Encoding: ensure no info leakage and correct mapping.
Chapter 5-Column Transformer in ML – Apply different transforms to different columns reliably and reproducibly.
Module 5. Machine Learning AlgorithmsChapter 1. Tensors
Chapter 1. Tensors
- 1D, 2D,3D ,N-D tensors
Chapter 2. ML Lab setup
- Installing anaconda
- Working wit Jupiter Notebook.
Chapter 3. Simple Linear Regression
- Graphical explanation
- Mathematical formulation
- Explanation with real data set: Understanding from testers point of view
Chapter 4. Regression Metrics : Testing the efficiency of the regressions.
- MSE: Mean absolute error – finding absolute error in ML prediction.
- MAE: mean squared error – Penalize large deviations.
- RMSE: Root mean squared error- Interpretable in natural units.
- calculation of R2 score
- Why testers use these metrics?
- validation of numerical accuracy.
- Detect drift in ML prediction.
- Compare multiple models.
Chapter 5. Multiple Linear Regression
- Mathematical understanding
- Comparison to simple liner regression
- Problem with MLR
Chapter 6. Gradient descent
- Intuition
- Mathematical formulation
- Universality of gradient descent
- Loss function
- convex vs. non-convex function
- Brief on the types of gradient descent
Chapter 7. Polynomial regression
- Mathematical and graphical formulation
- Hyperparamter
- overfitting Vs under-fitting
Chapter 8. Bias–Variance Tradeoff –
- what are Bias and variance?
- Why It Matters:
- Detect generalization issues
- Confirm overfitting/underfitting behavior
- Regularization
- Ridge regularization
- Lasso regularization
Chapter 9. Logistic regression.
- Mathematical overview.
- Graphical explanation
- Analysis with real world data set.
Chapter 10. Decision Tree
- Testing for overfitting
- split correctness
- interpretability.
Chapter 11. Random Forest
- Overview with mathematical and graphical points.
- Testing OOB score feature importance
- reliability, robustness.
Chapter 12. Misc. overview of ML.
- K Mean clustering
- KNN
- naive Bayes
- ROC Curve & AUC: TPR vs FPR visualization; ranking quality of classifiers.
Module 6. Natural Language Processing(NLP)
Chapter 1.what is NLP?
Chapter 2.Examples of NLP in real life.
Chapter 3.NLP Pipeline.
- Data Acquisition
- Text Pre-processing
- Feature Engineering
- Modelling
- Evaluation
Chapter 4.Text Pre-processing
- Tokenization
- Stemming Vs. Lemmatization
- Advanced preprocessing
- POS tagging
Chapter 5.Feature engg
- Key concepts
- Bag of words
- Tf-IDf
- One hot Encoding
- Word2Vec
Chapter 6.Evaluation of NLP: Model testing/Evaluation using the metrics.
- Accuracy
- Precision
- Recall
- F1 score
Module 7. Basics of Generative AI using LangChain.
Chapter 1. what is LangChain?
Chapter 2. Why Langchain?
Chapter 3. LangChain Components
- LLMs
- Prompts Tools
- Agents
- Memory.
- Vector Stores – Testing embedding correctness
Chapter 4. N8N tool for langchain: UI Automation tool for langchain
Chapter 5. Retrieval Augmented Generation (RAG)
- Ensures factual correctness and reduceshallucinations.
- Testing Includes:
- Retrieval precision and recall
- Chunking boundaries
- Evidence alignment
- Multilingual retrieval accuracy
Module 8. Basic overview of Deep Learning
Chapter 1. what are perceptrons?
- History of DL and perceptrons
- Mathematical overview.
- Loss function
Chapter 2.Generative AI overiew
Chapter 3. ANN – Fully connected network validation.
Chapter 4. CNN – Spatial feature extraction correctness.
Chapter 5. RNN – Sequence prediction stability.
Chapter 6. Transformers – Attention mechanism correctness.
Chapter 7. Decoder Architecture – Stable inference pipeline validation.
Module 9. Capstone Project
- AI-driven Test Case Generation – Validate extraction accuracy and Model testing & Optimization.
- AI Execution Validation – Model development(basic), testing & Optimization.