Hands-on Predicitive Analytics with Python

Unlock the power of predictive analytics with our hands-on Python course and master cutting-edge machine learning models like KNN and Random Forests, perfect for data-driven professionals eager to leverage the Python data science ecosystem.

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Essential Skills Gained

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Understand the main concepts and principles of predictive analytics

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Implement end-to-end predictive analytics projects using Python

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Explore advanced predictive modeling algorithms with intuitive explanations

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Deploy predictive model results as interactive applications

Format

  • Instructor-led
  • 3 days with lectures and hands-on labs.

Audience

  • Python developers
  • Data scientists
  • Machine learning engineers
  • Business analysts

Description

Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. 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, Seaborn, 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.

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Upcoming Course Dates

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Course Outline

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The Predictive Analytics Process

  1. Technical requirements

  2. What is predictive analytics?

  3. Reviewing important concepts of predictive analytics

  4. The predictive analytics process

  5. A quick tour of Python's data science stack

Problem Understanding and Data Preparation

  1. Technical requirements

  2. Understanding the business problem and proposing a solution

  3. Practical project – diamond prices

  4. Practical project – credit card default

Dataset Understanding – Exploratory Data Analysis

  1. Technical requirements

  2. What is EDA?

  3. Univariate EDA

  4. Bivariate EDA

  5. Introduction to graphical multivariate EDA

Predicting Numerical Values with Machine Learning

  1. Technical requirements

  2. Introduction to ML

  3. Practical considerations before modeling

  4. MLR

  5. Lasso regression

  6. KNN

  7. Training versus testing error

Predicting Categories with Machine Learning

  1. Technical requirements

  2. Classification tasks

  3. Credit card default dataset

  4. Logistic regression

  5. Classification trees

  6. Random forests

  7. Training versus testing error

  8. Multiclass classification

  9. Naive Bayes classifiers

Introducing Neural Nets for Predictive Analytics

  1. Technical requirements

  2. Introducing neural network models

  3. Introducing TensorFlow and Keras

  4. Regressing with neural networks

  5. Classification with neural networks

  6. The dark art of training neural networks

Model Evaluation

  1. Technical requirements

  2. Evaluation of regression models

  3. Evaluation for classification models

  4. The k-fold cross-validation

Model Tuning and Improving Performance

  1. Technical requirements

  2. Hyperparameter tuning

  3. Improving performance

Implementing a Model with Dash

  1. Technical requirements

  2. Model communication and/or deployment phase

  3. Introducing Dash

  4. Implementing a predictive model as a web application

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