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Machine Learning for Data Analytics with Python

Published by O'Reilly Media, Inc.

Intermediate content levelIntermediate

Transforming data into strategic business decisions

Course outcomes

  • Learn the principles of machine learning and how to apply them to solve business problems
  • Understand how to use Python and its key libraries for building and tuning ML models suitable for business analytics
  • Explore the process of extracting actionable insights from your data through predictive analytics and data-driven strategies
  • Learn to choose the right ML algorithm for business applications from customer segmentation to demand forecasting
  • Acquire strategies for interpreting ML model outcomes and translating them into strategic business decisions

Course description

In an era where data is king, business analysts and managers need a clear understanding of machine learning fundamentals and the skills to use Python’s robust libraries like scikit-learn, TensorFlow, and Keras in order to make a real impact on decision-making in a business context.

Join expert Chester Ismay to uncover the potential of machine learning to transform data into strategic assets. Through engaging hands-on exercises, you’ll learn to construct, assess, and fine-tune machine learning models, and you’ll explore how other organizations have succeeded with ML for data analytics.

What you’ll learn and how you can apply it

  • Develop ML models that can predict outcomes and trends relevant to your business domain
  • Apply data preprocessing techniques to structure your business data, making it suitable for ML analysis
  • Evaluate the performance of your ML models and understand the implications of various metrics in a business context
  • Present your findings through clear, impactful narratives that inform decision-making processes and strategic planning within your organization

This live event is for you because...

  • You’re a business analyst, data analyst, or business intelligence specialist looking to leverage ML to enhance your analytical capabilities and drive business growth.
  • You want to understand the practical applications of ML in business contexts to improve decision-making and operational efficiency.
  • You’re in a strategic role and want to harness predictive analytics to anticipate market trends, customer behavior, and business opportunities.

Prerequisites

  • Experience with Python and data analysis

Recommended preparation:

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Machine learning essentials for business data analytics (15 minutes)

  • Presentation: Introduction to machine learning in business contexts
  • Group discussion: Which ML application/topic are you most excited about?
  • Hands-on exercise: Set up the Python and ML environment
  • Q&A

Understanding data and preprocessing (50 minutes)

  • Presentation: The role of data in ML; exploratory data analysis
  • Hands-on exercises: Explore data collection and preprocessing techniques with pandas; explore visual data analysis with pandas and Matplotlib
  • Q&A
  • Break

Supervised learning for business decisions (65 minutes)

  • Presentation: Introduction to supervised learning and key algorithms; classification techniques for customer segmentation
  • Hands-on exercises: Build a regression model for pricing optimization; implement a classification model with scikit-learn
  • Q&A
  • Break

Unsupervised learning and pattern discovery (50 minutes)

  • Presentation: Exploring unsupervised learning and its business applications; clustering for customer insights
  • Hands-on exercises: Explore market basket analysis using association rules; explore k-means clustering with scikit-learn
  • Q&A
  • Break

Implementing and evaluating ML models (60 minutes)

  • Presentation: Model selection, training, and evaluation metrics; deploying ML models in business settings
  • Hands-on exercises: Explore cross-validation and hyperparameter tuning for model optimization; explore case studies—from model development to business strategy
  • Q&A

Your Instructor

  • Chester Ismay

    Dr. Chester Ismay is an experienced data science educator and consultant. Chester enjoys helping others get into data science, figuring out how to best practice and improve their skills. He is co-author of "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at https://moderndive.com/v2/ He likes leading education and data science teams to improve best practices based on data from the learning sciences. Throughout his career, he has worked in academia, as a corporate trainer, at tech bootcamps, and as an independent consultant in the fields of education, insurance, and sports analytics.

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Skills covered

  • Machine Learning
  • Python