Machine Learning Practitioner E-Learning Training

The ML Practitioner Learning Kit is a hands-on technical training package designed to help learners design, optimize and deploy real-world machine learning models. It is ideal for data scientists, machine learning engineers and analytics practitioners who want to move beyond foundational ML knowledge and build production-grade skills.

Demo Machine Learning Practitioner Training

Rather than focusing mainly on theory or high-level strategy, this Learning Kit emphasizes the practical disciplines that determine whether a model succeeds in production. You will work with data preparation, feature engineering, anomaly detection, hyperparameter tuning, MLOps, deployment and reinforcement learning.

Across five practical sections, you will progress from raw data to clean, model-ready datasets, then move into advanced training, tuning, deployment, monitoring and emerging paradigms such as reinforcement learning.

What will you learn?

• Prepare data for machine learning models
• Handle missing values and outliers
• Encode categorical data and scale numeric features
• Build feature engineering pipelines with scikit-learn
• Apply feature selection and dimensionality reduction
• Optimize hyperparameters using modern tuning tools
• Detect anomalies, fraud, errors and rare events
• Apply MLOps principles for model deployment and lifecycle management
• Train, register and deploy models using MLflow on Databricks
• Understand and apply reinforcement learning concepts

Who should attend?

This Learning Kit is suitable for:

• Data Scientists
• Machine Learning Engineers
• Analytics Practitioners
• Data Analysts moving into machine learning
• AI Developers
• MLOps Engineers
• Data professionals who want to build production-ready models
• Professionals who want to develop practical ML skills

This LearningKit with more than 21 hours of learning is divided into three tracks:

Learning Kit content

Section 1: Feature Engineering for ML Models

This section lays the foundation for high-performing machine learning models by focusing on data preparation and feature engineering. You will learn how to handle missing values and outliers, encode categorical variables and scale numeric features so that data is model-ready.

Courses:

• Handling Missing Values and Outliers in Data
• Encoding Categorical Data for Machine Learning
• Scaling Numeric Data for Machine Learning
• Feature Engineering Techniques for Machine Learning
• Feature Selection and Dimensionality Reduction

Section 2: Hyperparameter Tuning for Machine Learning

This section focuses on hyperparameter tuning, one of the most important drivers of model performance. You will learn the difference between model parameters and hyperparameters and explore techniques such as grid search, random search and cross-validation.

Courses:

• Hyperparameter Tuning Techniques
• Hyperparameter Tuning with scikit-learn
• Hyperparameter Tuning with Hyperopt on Databricks
• Hyperparameter Tuning with Ray Tune on Databricks
• Hyperparameter Tuning with Optuna
• Automated Machine Learning with H2O AutoML
• Hyperparameter Tuning with Keras Tuner

Section 3: Anomaly Detection

This section equips you with practical techniques to identify outliers, errors, fraud patterns and rare events that can affect data quality and model performance.

Courses:

• Understanding Anomalies and Their Detection
• Using Z-Scores and IQR for Anomaly Detection
• Using LOF, iForest, and One-Class SVMs for Anomaly Detection

Section 4: MLOps and Model Deployment

This section bridges the gap between model development and production. You will learn how MLOps extends DevOps practices to address machine learning-specific challenges such as data drift, model retraining, reproducibility, lifecycle management, CI/CD, infrastructure as code, containerization and automated testing.

Courses:

• MLOps and Model Deployment: Contextualizing MLOps and DevOps
• MLOps and Model Deployment: Model Deployment and Serving Strategies
• MLOps and Model Deployment: Training and Deploying Models Using MLflow on Databricks

Section 5: Reinforcement Learning

This section introduces reinforcement learning, a machine learning paradigm in which agents learn optimal behavior through trial, feedback and reward instead of labeled data.

Courses:

• Contextualizing Reinforcement Learning
• Implementing Simple Reinforcement Learning Methods