Machine Learning & AI Fundamentals: Practice Exams

Machine Learning & AI Fundamentals: Practice Exams – taught by Himanshu Kaushik, is a high‑impact Udemy course that prepares you for real‑world data‑science interviews. Updated July 2026, this advanced training delivers 200 scenario‑driven questions that mirror FAANG screening assessments. Learners gain hands‑on practice with TensorFlow, Keras, and Scikit‑Learn while mastering evaluation metrics essential for production‑grade AI systems. The course also offers a free coupon that unlocks 100 % off for a limited time, making it one of the most valuable free machine learning course options today.

What You'll Learn

• Build end‑to‑end machine‑learning pipelines that prevent data leakage and streamline model deployment.
• Master TensorFlow and Keras architectures, configuring loss functions, activation layers, and regularization techniques.
• Learn to differentiate supervised, unsupervised, and reinforcement learning algorithms for complex data problems.
• Understand Scikit‑Learn’s RandomizedSearchCV for efficient hyper‑parameter optimization across large search spaces.
• Create realistic practice‑exam scenarios that simulate FAANG data‑science interview questions.
• Implement precision, recall, F1‑score, and RMSE calculations tailored to specific business objectives.
• Analyze transformer model designs for real‑time sentiment analysis on social‑media streams.
• Apply model‑interpretability methods to explain predictions in high‑stakes domains such as healthcare and finance.

Course Details

• Instructor: Himanshu Kaushik
• Rating: 4.4 stars (based on student reviews)
• Level: Advanced
• Language: English (US)
• Certificate: Yes, upon completion
• Includes: Lifetime access, mobile‑friendly video streaming, downloadable practice‑exam PDFs

What This Course Covers

Supervised vs. Unsupervised Learning

• Distinguish key characteristics of supervised, unsupervised, and reinforcement learning paradigms.
• Select appropriate algorithms for classification, regression, clustering, and policy‑learning tasks.
• Evaluate model suitability using bias‑variance trade‑off analysis.
• Review case studies from finance, healthcare, and e‑commerce sectors.

Deep Learning with TensorFlow & Keras

• Construct convolutional neural networks (CNNs) for image‑recognition challenges.
• Build recurrent neural networks (RNNs) and transformers for sequential data processing.
• Tune learning rates, batch sizes, and optimizer settings to avoid overfitting.
• Deploy trained models to cloud platforms using TensorFlow Serving.

Scikit‑Learn Pipelines & Hyperparameter Tuning

• Assemble preprocessing, feature‑engineering, and modeling steps into reusable pipelines.
• Prevent data leakage by applying cross‑validation correctly within pipelines.
• Execute RandomizedSearchCV and GridSearchCV to discover optimal hyper‑parameters.
• Interpret tuning results with visualizations and statistical summaries.

Model Evaluation & Business Metrics

• Compute precision, recall, F1‑score, ROC‑AUC, and RMSE for diverse problem types.
• Align evaluation metrics with business goals such as fraud detection or medical diagnosis.
• Perform calibration checks to ensure probability estimates are reliable.
• Generate confusion matrices and classification reports for stakeholder communication.

Practice Exam Scenarios

• Solve 200 realistic questions covering regression, classification, NLP, and computer vision.
• Analyze scenario‑based prompts that require code snippets, mathematical derivations, and model‑selection reasoning.
• Review detailed answer explanations that highlight common interview pitfalls.
• Track progress with built‑in self‑assessment tools and performance dashboards.

Who Should Take This Course

• Aspiring data scientists targeting senior‑level positions at FAANG or top research labs.
• Machine‑learning engineers seeking to validate theoretical knowledge through practice tests.
• Professionals transitioning from software development to AI‑focused roles.
• Academic researchers preparing for thesis defenses that involve advanced ML models.
• Technical interview coaches who need a curated bank of high‑quality practice questions.

Prerequisites

• Proficiency in Python programming, including libraries such as NumPy and Pandas.
• Fundamental understanding of linear algebra, calculus, and probability theory.
• Prior exposure to basic machine‑learning concepts and at least one framework (TensorFlow, PyTorch, or Scikit‑Learn).