Making Predictions with Data and Python

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    Course

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    Online

  • Start date

    Different dates available

Build Awesome Predictive Models with PythonPython has become one of any data scientist's favorite tools for doing Predictive Analytics. In this hands-on course, you will learn how to build predictive models with Python.During the course, we will talk about the most important theoretical concepts that are essential when building predictive models for real-world problems. The main tool used in this course is scikit -learn, which is recognized as a great tool: it has a great variety of models, many useful routines, and a consistent interface that makes it easy to use. All the topics are taught using practical examples and throughout the course, we build many models using real-world datasets.By the end of this course, you will learn the various techniques in making predictions about bankruptcy and identifying spam text messages and then use our knowledge to create a credit card using a linear model for classification along with logistic regression.About the AuthorAlvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years' experience in analytical roles..
He worked in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as: Business, Education, Psychology, and Mass Media. He also has taught many (online and on-site) courses to students from around the world in topics such as Data Science, Mathematics, Statistics, R programming, and Python. Alvaro Fuentes is a big Python fan, has been working with it for about 4 years, and uses it routinely for analyzing data and producing predictions. He has also used it in a couple of software projects. He is also a big R fan, and doesn't like the controversy inherent in any attempt to evaluate which is the best—R or Python; he uses them both

Facilities

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Online

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Different dates availableEnrolment now open

About this course

Understand the main concepts and principles of Predictive Analytics and how to use them when building real-world predictive models.
Properly use scikit-learn, the main Python library for Predictive Analytics and Machine Learning.
Learn the types of Predictive Analytics problem and how to apply the main models and algorithms to solve real world problems.
Build, evaluate, and interpret classification and regression models on real-world datasets.
Understand Regression and Classification
Refresh your visualization skills

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This centre's achievements

2021

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The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 4 years

Subjects

  • GCSE Mathematics
  • Install
  • Mathematics
  • Computing
  • Database
  • Data analysis
  • Data Management
  • Information Systems
  • Information Systems management
  • Database training

Course programme

The Tools for Doing Predictive Analytics with Python 5 lectures 34:12 The Course Overview This video provides an overview of the entire course. The Anaconda Distribution Explain what the Anaconda Distribution is and why we are using it in this course. Also to show how to get and install the software.
  • Explain what is the Anaconda Distribution and the problem it solves
  • Go to the website to get Anaconda
  • Show where to find the installer and ask the user to install it
The Jupyter Notebook Introduce the computing environment in which we will work for the rest of the course.
  • Explain what is the Jupyter Notebook
  • How to start the Jupyter Notebook from the command line
  • Take a tour to see the interphase of Jupyter and show how to edit code and markdown cells
NumPy - The Foundation for Scientific Computing Explain what is NumPy, the problem it solves and why is important for Python’s Data Stack. Also show some of the most common ways to create ndarrays and how to operate with them.
  • Explain what is NumPy
  • Show what is a vectorized operation using simple examples
  • Show some of the most common ways to create ndarrays and how to perform mathematical functions on them
Using Pandas for Analyzing Data Explain what Pandas is and what we can do with it. Talk about the main objects in this library, that is, Series and DataFrames.
  • Explain what is pandas and what it is used for
  • Explain what is Series and a DataFrame
  • Provide practical examples of creation and manipulation of Pandas objects
The Tools for Doing Predictive Analytics with Python 5 lectures 34:12 The Course Overview This video provides an overview of the entire course. The Anaconda Distribution Explain what the Anaconda Distribution is and why we are using it in this course. Also to show how to get and install the software.
  • Explain what is the Anaconda Distribution and the problem it solves
  • Go to the website to get Anaconda
  • Show where to find the installer and ask the user to install it
The Jupyter Notebook Introduce the computing environment in which we will work for the rest of the course.
  • Explain what is the Jupyter Notebook
  • How to start the Jupyter Notebook from the command line
  • Take a tour to see the interphase of Jupyter and show how to edit code and markdown cells
NumPy - The Foundation for Scientific Computing Explain what is NumPy, the problem it solves and why is important for Python’s Data Stack. Also show some of the most common ways to create ndarrays and how to operate with them.
  • Explain what is NumPy
  • Show what is a vectorized operation using simple examples
  • Show some of the most common ways to create ndarrays and how to perform mathematical functions on them
Using Pandas for Analyzing Data Explain what Pandas is and what we can do with it. Talk about the main objects in this library, that is, Series and DataFrames.
  • Explain what is pandas and what it is used for
  • Explain what is Series and a DataFrame
  • Provide practical examples of creation and manipulation of Pandas objects
The Course Overview This video provides an overview of the entire course. The Course Overview This video provides an overview of the entire course. The Course Overview This video provides an overview of the entire course. The Course Overview This video provides an overview of the entire course. This video provides an overview of the entire course. This video provides an overview of the entire course. The Anaconda Distribution Explain what the Anaconda Distribution is and why we are using it in this course. Also to show how to get and install the software.
  • Explain what is the Anaconda Distribution and the problem it solves
  • Go to the website to get Anaconda
  • Show where to find the installer and ask the user to install it
The Anaconda Distribution Explain what the Anaconda Distribution is and why we are using it in this course. Also to show how to get and install the software.
  • Explain what is the Anaconda Distribution and the problem it solves
  • Go to the website to get Anaconda
  • Show where to find the installer and ask the user to install it
The Anaconda Distribution Explain what the Anaconda Distribution is and why we are using it in this course. Also to show how to get and install the software.
  • Explain what is the Anaconda Distribution and the problem it solves
  • Go to the website to get Anaconda
  • Show where to find the installer and ask the user to install it
The Anaconda Distribution Explain what the Anaconda Distribution is and why we are using it in this course. Also to show how to get and install the software.
  • Explain what is the Anaconda Distribution and the problem it solves
  • Go to the website to get Anaconda
  • Show where to find the installer and ask the user to install it
Explain what the Anaconda Distribution is and why we are using it in this course. Also to show how to get and install the software.
  • Explain what is the Anaconda Distribution and the problem it solves
  • Go to the website to get Anaconda
  • Show where to find the installer and ask the user to install it
Explain what the Anaconda Distribution is and why we are using it in this course. Also to show how to get and install the software.
  • Explain what is the Anaconda Distribution and the problem it solves
  • Go to the website to get Anaconda
  • Show where to find the installer and ask the user to install it
The Jupyter Notebook Introduce the computing environment in which we will work for the rest of the course.
  • Explain what is the Jupyter Notebook
  • How to start the Jupyter Notebook from the command line
  • Take a tour to see the interphase of Jupyter and show how to edit code and markdown cells
The Jupyter Notebook Introduce the computing environment in which we will work for the rest of the course.
  • Explain what is the Jupyter Notebook
  • How to start the Jupyter Notebook from the command line
  • Take a tour to see the interphase of Jupyter and show how to edit code and markdown cells
The Jupyter Notebook Introduce the computing environment in which we will work for the rest of the course.
  • Explain what is the Jupyter Notebook
  • How to start the Jupyter Notebook from the command line
  • Take a tour to see the interphase of Jupyter and show how to edit code and markdown cells
The Jupyter Notebook Introduce the computing environment in which we will work for the rest of the course.
  • Explain what is the Jupyter Notebook
  • How to start the Jupyter Notebook from the command line
  • Take a tour to see the interphase of Jupyter and show how to edit code and markdown cells
Introduce the computing environment in which we will work for the rest of the course.
  • Explain what is the Jupyter Notebook
  • How to start the Jupyter Notebook from the command line
  • Take a tour to see the interphase of Jupyter and show how to edit code and markdown cells
Introduce the computing environment in which we will work for the rest of the course.
  • Explain what is the Jupyter Notebook
  • How to start the Jupyter Notebook from the command line
  • Take a tour to see the interphase of Jupyter and show how to edit code and markdown cells
NumPy - The Foundation for Scientific Computing Explain what is NumPy, the problem it solves and why is important for Python’s Data Stack. Also show some of the most common ways to create ndarrays and how to operate with them.
  • Explain what is NumPy
  • Show what is a vectorized operation using simple examples
  • Show some of the most common ways to create ndarrays and how to perform mathematical functions on them
NumPy - The Foundation for Scientific Computing Explain what is NumPy, the problem it solves and why is important for Python’s Data Stack. Also show some of the most common ways to create ndarrays and how to operate with them.
  • Explain what is NumPy
  • Show what is a vectorized operation using simple examples
  • Show some of the most common ways to create ndarrays and how to perform mathematical functions on them
NumPy - The Foundation for Scientific Computing Explain what is NumPy, the problem it solves and why is important for Python’s Data Stack. Also show some of the most common ways to create ndarrays and how to operate with them.
  • Explain what is NumPy
  • Show what is a vectorized operation using simple examples
  • Show some of the most common ways to create ndarrays and how to perform mathematical functions on them
NumPy - The Foundation for Scientific Computing Explain what is NumPy, the problem it solves and why is important for Python’s Data Stack. Also show some of the most common ways to create ndarrays and how to operate with them.
  • Explain what is NumPy
  • Show what is a vectorized operation using simple examples
  • Show some of the most common ways to create ndarrays and how to perform mathematical functions on them
Explain what is NumPy, the problem it solves and why is important for Python’s Data Stack. Also show some of the most common ways to create ndarrays and how to operate with them.
  • Explain what is NumPy
  • Show what is a vectorized operation using simple examples
  • Show some of the most common ways to create ndarrays and how to perform mathematical functions on them
Explain what is NumPy, the problem it solves and why is important for Python’s Data Stack. Also show some of the most common ways to create ndarrays and how to operate with them.
  • Explain what is NumPy
  • Show what is a vectorized operation using simple examples
  • Show some of the most common ways to create ndarrays and how to perform mathematical functions on them
Using Pandas for Analyzing Data Explain what Pandas is and what we can do with it. Talk about the main objects in this library, that is, Series and DataFrames.
  • Explain what is pandas and what it is used for
  • Explain what is Series and a DataFrame
  • Provide practical examples of creation and manipulation of Pandas objects
Using Pandas for Analyzing Data Explain what Pandas is and what we can do with it. Talk about the main objects in this library, that is, Series and DataFrames.
  • Explain what is pandas and what it is used for
  • Explain what is Series and a DataFrame
  • Provide practical examples of creation and manipulation of Pandas objects
Using Pandas for Analyzing Data Explain what Pandas is and what we can do with it. Talk about the main objects in this library, that is, Series and DataFrames.
  • Explain what is pandas and what it is used for
  • Explain what is Series and a DataFrame
  • Provide practical examples of creation and manipulation of Pandas objects
Using Pandas for Analyzing Data Explain what Pandas is and what we can do with it. Talk about the main objects in this library, that is, Series and DataFrames.
  • Explain what is pandas and what it is used for
  • Explain what is Series and a DataFrame
  • Provide practical examples of creation and manipulation of Pandas objects
Explain what Pandas is and what we can do with it. Talk about the main objects in this library, that is, Series and DataFrames.
  • Explain what is pandas and what it is used for
  • Explain what is Series and a DataFrame
  • Provide practical examples of creation and manipulation of Pandas objects
Explain what Pandas is and what we can do with it. Talk about the main objects in this library, that is, Series and DataFrames.
  • Explain what is pandas and what it is used for
  • Explain what is Series and a DataFrame
  • Provide practical examples of creation and manipulation of Pandas objects
Visualization Refresher 3 lectures 23:40 Plotting with Matplotlib Explain to the viewer what is matplotlib and what are the main concepts used when working with this library.
  • Explain what is the matplotlib library and its use
  • Explain the main terms used when working with matplotlib
  • Provide simple examples of visualizations produced with matplotlib
Visualizing data with Pandas Show some of the visualization capabilities included in pandas objects and how we can modify some elements of a pandas plot with matplotlib.
  • Show how to produce some common visualizations using the methods from pandas objects
  • Show to modify elements of a pandas plot with matplotlib
  • Give a list of the plots that can be produced with pandas
Statistical Visualization with Seaborn Introduce the Seaborn library and show some of the specialized and complex statistical visualizations that can be produced with this library.
  • Explain what is Seaborn
  • Show some examples of commonly used plots produced with Seaborn
  • Show examples of complex plots produced with Seaborn
Visualization Refresher. 3 lectures 23:40 Plotting with Matplotlib Explain to the viewer what is matplotlib and what are the main concepts used when working with this library.
  • Explain what is the matplotlib library and its use
  • Explain the main terms used when working with matplotlib
  • Provide simple examples of visualizations produced with matplotlib
Visualizing data with Pandas Show some of the visualization capabilities included in pandas objects and how we can modify some elements of a pandas plot with matplotlib.
  • Show how to produce some common visualizations using the methods from pandas objects
  • Show to modify elements of a pandas plot with matplotlib
  • Give a list of the plots that can be produced with pandas
Statistical Visualization with Seaborn Introduce the Seaborn library and show some of the specialized and complex statistical visualizations that can be produced with this library.
  • Explain what is Seaborn
  • Show some examples of commonly used plots produced with Seaborn
  • Show examples of complex plots produced with Seaborn
Plotting with Matplotlib Explain to the viewer what is matplotlib and what are the main concepts used when working with this library.
  • Explain what is the matplotlib library and its use
  • Explain the main terms used when working with matplotlib
  • Provide simple examples of visualizations produced with matplotlib
Plotting with Matplotlib Explain to the viewer what is matplotlib and what are the main concepts used when working with this library mathematical, statistical and machine...

Additional information

Knowledge of the Python programming language is assumed. Basic familiarity with Python's Data Science Stack would be useful, although a brief review is given. Familiarity with basic mathematics and statistical concepts is also advantageous to take full advantage of this course.

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Making Predictions with Data and Python

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