Machine Learning with Databricks

Exploring Data Science Techniques and Advanced Analytics

Duration

3 Days (7 hours per day)

Level

Intermediate Level

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As per your team needs

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This Machine Learning course on Databricks is designed to equip participants with the skills to apply machine learning techniques using Databricks’ unified analytics platform. The course combines theoretical knowledge with practical applications through hands-on sessions, enabling participants to implement machine learning models effectively.

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Data Scientists and Machine Learning Engineers looking to utilize Databricks for machine learning projects
Software Engineers and Analysts interested in data science and machine learning
Technical Project Managers overseeing data-driven projects

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Getting Started with Databricks

Overview of Databricks platform and ecosystem
Setting up Databricks environment
Introduction to Databricks Notebooks and Workspaces
Hands-On: Creating and managing a Databricks workspace and notebook

Data Handling and Preprocessing

Data ingestion in Databricks
Data manipulation using PySpark DataFrame API
Handling missing data and data normalization
Hands-On: Preprocessing a dataset using Databricks for a machine learning project

Exploratory Data Analysis (EDA)

Visualization tools in Databricks
Descriptive statistics and data summarization
Correlation analysis and feature selection
Hands-On: Performing EDA on a sample dataset

Machine Learning with MLlib

Overview of MLlib in Spark
Regression and classification algorithms
Clustering and dimensionality reduction
Hands-On: Building a regression model using MLlib

Advanced Machine Learning Techniques

Introduction to ensemble methods
Overfitting and model tuning with CrossValidator and TrainValidationSplit
Using pipelines for model workflows
Hands-On: Creating and tuning a classification pipeline

Deep Learning on Databricks

Introduction to deep learning libraries (TensorFlow, Keras)
Integration of deep learning frameworks with Spark
Building and training neural networks
Hands-On: Implementing a simple neural network for image classification

Model Management and Deployment

Tracking experiments with MLflow
Model versioning and staging
Deployment strategies for machine learning models
Hands-On: Using MLflow for model tracking and deployment

Real-Time Machine Learning

Introduction to Structured Streaming for real-time analytics
Applying machine learning models to streaming data
Performance considerations and optimizations
Hands-On: Developing a streaming application that uses a machine learning model

Advanced Topics and Case Studies

Best practices in machine learning lifecycle management
Case studies on successful machine learning projects in Databricks
Future trends in machine learning on cloud platforms
Hands-On: Analyzing a case study and brainstorming project ideas

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Basic understanding of Python programming
Fundamental knowledge of data science and machine learning concepts
Experience with Apache Spark or big data platforms (preferable)

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Machine Learning with Databricks

Duration

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