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Catalog / All levels / Databricks Certified Machine Learning Associate Exam Preparation with 10 Practice Exams
Mammoth Club All levels 16 sections 100 lectures

Databricks Certified Machine Learning Associate Exam Preparation with 10 Practice Exams

You understand the theory behind machine learning. But how do you apply it in a real-world, collaborative environment? This program teaches you the hands-on process for building, managing, and deploying ML models on the Databricks platform.

MC
Created by Team Mammoth
01
Skill level
All levels
02
Sections
16
03
Lectures
100
04
Instructor
Team Mammoth
What's inside

This course includes.

✓
16
Sections
✓
100
Lectures
✓
3
Resources
✓
60
Quizzes
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content

Curriculum & lectures.

6 sections · 52 lectures
+ Welcome 2 lectures
What You'll Learn Locked
What You'll Need Locked
+ Databricks Machine Learning 19 lectures
Best practices of an MLOps strategy Locked
Advantages of using ML runtimes Locked
AutoML on Databricks Locked
Identify how AutoML facilitates model and feature selection Locked
Identify the advantages AutoML brings to the model development process Locked
Create a feature store table in Unity Catalog Locked
Benefits of creating feature store tables at the account level in Unity Catalog vs at the workspace Locked
Write data to a feature store table Locked
Train a model with features from a feature store table Locked
Score a model using features from a feature store table Locked
Differences between online and offline feature tables Locked
Identify the best run using the MLflow Client API Locked
Manually log metrics, artifacts, and models in an MLflow Run Locked
Identify information available in the MLFlow UI Locked
Register a model using the MLflow Client API in the Unity Catalog registry Locked
Benefits of registering models in the Unity Catalog registry over the workspace registry Locked
Identify scenarios where promoting code is preferred over promoting models and vice versa Locked
Set or remove a tag for a model Locked
Promote a challenger model to a champion model using aliases Locked
+ Data Processing 9 lectures
Compute summary statistics on a Spark DataFrame using .summary() or dbutils data summaries Locked
Remove outliers from a Spark DataFrame based on standard deviation or IQR Locked
Create visualizations for categorical or continuous features Locked
Compare two categorical or two continuous features using the appropriate method Locked
Compare and contrast imputing missing values with the mean or median or mode value Locked
Impute missing values with the mode, mean, or median value Locked
Use one-hot encoding for categorical features Locked
Model types or data sets for which one-hot encoding is or is not appropriate Locked
Identify scenarios where log scale transformation is appropriate Locked
+ Model Development 15 lectures
Use ML foundations to select the appropriate algorithm for a given model scenario Locked
Methods to mitigate data imbalance in training data Locked
Compare estimators and transformers Locked
Develop a training pipeline Locked
Use Hyperopt's fmin operation to tune a model's hyperparameters Locked
Perform random or grid search or Bayesian search for tuning hyperparameters Locked
Parallelize single node models for hyperparameter tuning Locked
Benefits and downsides of using cross-validation over a train-validation split Locked
Perform cross-validation as a part of model fitting Locked
Identify the number of models being trained through grid-search and cross-validation Locked
Use common classification metrics: F1, Log Loss, ROC AUC, etc Locked
Use common regression metrics: RMSE, MAE, R-squared, etc Locked
Choose the most appropriate metric for a given scenario objective Locked
Exponentiate log-transformed variables before calculating evaluation metrics or interpreting predict Locked
Impact of model complexity and the bias variance tradeoff on performance Locked
+ Model Deployment 6 lectures
Differences and advantages of model serving approaches: batch, realtime and streaming Locked
Deploy a custom model to a model endpoint Locked
Use pandas to perform batch inference Locked
Identify how streaming inference is performed with Delta Live Tables Locked
Deploy and query a model for realtime inference Locked
Split data between endpoints for realtime interference Locked
+ Challenge Your 10 FREE Practice Exams 1 lecture
Where to go from here Locked
Description

About this course.

Learn the fundamentals of the machine learning lifecycle on Databricks, from data preparation and feature engineering to model training, evaluation, and experiment tracking with MLflow.

✅ Learn to perform exploratory data analysis (EDA) and scalable feature engineering to prepare data for machine learning.

✅ Understand and apply core machine learning models for classification and regression using tools like scikit-learn and Spark MLlib.

✅ Use MLflow Tracking to log experiments, compare model runs, and ensure the reproducibility of your results.

✅ Explore the basics of the model lifecycle, including model evaluation, selection, and an introduction to deployment concepts.

Whether you are a data scientist new to the Databracks ecosystem or an analyst looking to move into machine learning, this course provides the essential skills to manage the end-to-end ML process.

🎁 Includes 10 full-length practice exams.

Solidify your understanding of the ML lifecycle. Build with confidence.

If you're ready to move from ML theory to hands-on practice and build a strong foundation for your machine learning career, this is your starting block.

Ready to start building?

You understand the theory behind machine learning. But how do you apply it in a real-world, collaborative environment? This program teaches you the hands-on process for building, managing, and deploying ML models on the Databricks platform.

Buy lifetime access →