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.
Understand the main concepts and principles of predictive analytics
Implement end-to-end predictive analytics projects using Python
Explore advanced predictive modeling algorithms with intuitive explanations
Deploy predictive model results as interactive applications
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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What is predictive analytics?
Reviewing important concepts of predictive analytics
The predictive analytics process
A quick tour of Python's data science stack
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Understanding the business problem and proposing a solution
Practical project – diamond prices
Practical project – credit card default
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What is EDA?
Univariate EDA
Bivariate EDA
Introduction to graphical multivariate EDA
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Introduction to ML
Practical considerations before modeling
MLR
Lasso regression
KNN
Training versus testing error
Technical requirements
Classification tasks
Credit card default dataset
Logistic regression
Classification trees
Random forests
Training versus testing error
Multiclass classification
Naive Bayes classifiers
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Introducing neural network models
Introducing TensorFlow and Keras
Regressing with neural networks
Classification with neural networks
The dark art of training neural networks
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Evaluation of regression models
Evaluation for classification models
The k-fold cross-validation
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Hyperparameter tuning
Improving performance
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Model communication and/or deployment phase
Introducing Dash
Implementing a predictive model as a web application
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Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier
Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier