In this course, using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.


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

Learning Objectives

Working in a hands-on learning environment, guided by our expert team, attendees will learn to
Understand the main concepts and principles of predictive analytics
Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations
Learn to deploy a predictive model's results as an interactive application
Learn about the stages involved in producing complete predictive analytics solutions
Understand how to define a problem, propose a solution, and prepare a dataset
Use visualizations to explore relationships and gain insights into the dataset
Learn to build regression and classification models using scikit-learn
Use Keras to build powerful neural network models that produce accurate predictions
Learn to serve a model's predictions as a web application

  • The Predictive Analytics Process

  • Technical requirements
    What is predictive analytics?
    Reviewing important concepts of predictive analytics
    The predictive analytics process
    A quick tour of Python's data science stack

  • Problem Understanding and Data Preparation

  • Technical requirements
    Understanding the business problem and proposing a solution
    Practical project - diamond prices
    Practical project - credit card default

  • Dataset Understanding - Exploratory Data Analysis

  • Technical requirements
    What is EDA?
    Univariate EDA
    Bivariate EDA
    Introduction to graphical multivariate EDA

  • Predicting Numerical Values with Machine Learning

  • Technical requirements
    Introduction to ML
    Practical considerations before modeling
    Lasso regression
    Training versus testing error

  • Predicting Categories with Machine Learning

  • Technical requirements
    Classification tasks
    Credit card default dataset
    Logistic regression
    Classification trees
    Random forests
    Training versus testing error
    Multiclass classification
    Naive Bayes classifiers

  • Introducing Neural Nets for Predictive Analytics

  • Technical requirements
    Introducing neural network models
    Introducing TensorFlow and Keras
    Regressing with neural networks
    Classification with neural networks
    The dark art of training neural networks

  • Model Evaluation

  • Technical requirements
    Evaluation of regression models
    Evaluation for classification models
    The k-fold cross-validation

  • Model Tuning and Improving Performance

  • Technical requirements
    Hyperparameter tuning
    Improving performance

  • Implementing a Model with Dash

  • Technical requirements
    Model communication and/or deployment phase
    Introducing Dash
    Implementing a predictive model as a web application


This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer.




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


Length: 3.0 days (24 hours)


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