Computer Science Artificial intelligence Course
Dive into practical AI skills for tabular binary classification. Master data preparation, EDA, model building, hyperparameter tuning, performance evaluation, interpretability techniques, bias auditing, and ethical deployment strategies to develop trustworthy AI systems suitable for real-world tech applications in modern industries.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
This course guides you in creating robust tabular binary classification systems from the ground up. Gain expertise in data cleaning, preprocessing, feature engineering, and exploratory data analysis (EDA). Progress to model selection, training, tuning, and evaluation using practical metrics and visual tools. Explore dataset sourcing, model interpretability, bias detection, privacy measures, and secure deployment for reliable, fair, and production-grade AI solutions.
Elevify advantages
Develop skills
- Prepare quality ML datasets by addressing class imbalance, feature encoding, and numeric scaling.
- Conduct efficient EDA to identify data leaks, outliers, missing values, and quality problems.
- Develop and optimise ML models including baselines, tree ensembles, and hyperparameter strategies.
- Assess AI models using binary metrics, ROC/PR curves, threshold selection, and SHAP explanations.
- Ensure safe AI deployment through drift monitoring, bias auditing, and privacy safeguards.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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