This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data.

  • Data Exploration and Cleaning

  • Python and the Anaconda Package Management System
    Different Types of Data Science Problems
    Loading the Case Study Data with Jupyter and pandas
    Data Quality Assurance and Exploration
    Exploring the Financial History Features in the Dataset
    Activity 1- Exploring Remaining Financial Features in the Dataset

  • Introduction to Scikit-Learn and Model Evaluation

  • Introduction
    Model Performance Metrics for Binary Classification
    Activity 2- Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve

  • Details of Logistic Regression and Feature Exploration

  • Introduction
    Examining the Relationships between Features and the Response
    Univariate Feature Selection- What It Does and Doesn't Do
    Building Cloud-Native Applications
    Activity 3- Fitting a Logistic Regression Model and Directly Using the Coefficients

  • The Bias-Variance Trade-off

  • Introduction
    Estimating the Coefficients and Intercepts of Logistic Regression
    Cross Validation- Choosing the Regularization Parameter and Other Hyperparameters
    Activity 4- Cross-Validation and Feature Engineering with the Case Study Data

  • Decision Trees and Random Forests

  • Introduction
    Decision trees
    Random Forests- Ensembles of Decision Trees
    Activity 5- Cross-Validation Grid Search with Random Forest

  • Imputation of Missing Data, Financial Analysis, and Delivery to Client

  • Introduction
    Review of Modeling Results
    Dealing with Missing Data- Imputation Strategies
    Activity 6- Deriving Financial Insights
    Final Thoughts on Delivering the Predictive Model to the Client


If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.




While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.


Length: 2.0 days (16 hours)


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