Advanced Predictive Techniques with Scikit-Learn& TensorFlow

Advanced Predictive Techniques with Scikit-Learn& TensorFlow, available at $39.99, has an average rating of 4.3, with 18 lectures, 5 quizzes, based on 16 reviews, and has 145 subscribers.

You will learn about Use ensemble algorithms to combine many individual predictors to produce better predictions. Apply advanced techniques such as dimensionality reduction to combine features and build better models. Evaluate models and choose the optimal hyper-parameters using cross-validation. Learn the foundations for working and building models using Neural Networks. Learn different techniques to solve problems that arise when doing Predictive Analytics in the real world This course is ideal for individuals who are The course is for data analysts or data scientists, software engineers, and developers interested in learning advanced Predictive Analytics with Python. Business analysts/business Intelligence experts who would like to learn how to go from basic predictive models to building advanced models to produce better predictions will also find this course indispensable. or Knowledge of Python and familiarity with its Data Science Stack are assumed. Additionally, an understanding of the basic concepts of predictive analytics and how to use basic predictive models is also necessary to take full advantage of this course. It is particularly useful for The course is for data analysts or data scientists, software engineers, and developers interested in learning advanced Predictive Analytics with Python. Business analysts/business Intelligence experts who would like to learn how to go from basic predictive models to building advanced models to produce better predictions will also find this course indispensable. or Knowledge of Python and familiarity with its Data Science Stack are assumed. Additionally, an understanding of the basic concepts of predictive analytics and how to use basic predictive models is also necessary to take full advantage of this course.

Enroll now: Advanced Predictive Techniques with Scikit-Learn& TensorFlow

Summary

Title: Advanced Predictive Techniques with Scikit-Learn& TensorFlow

Price: $39.99

Average Rating: 4.3

Number of Lectures: 18

Number of Quizzes: 5

Number of Published Lectures: 18

Number of Published Quizzes: 5

Number of Curriculum Items: 23

Number of Published Curriculum Objects: 23

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Use ensemble algorithms to combine many individual predictors to produce better predictions.
Apply advanced techniques such as dimensionality reduction to combine features and build better models.
Evaluate models and choose the optimal hyper-parameters using cross-validation.
Learn the foundations for working and building models using Neural Networks.
Learn different techniques to solve problems that arise when doing Predictive Analytics in the real world

Who Should Attend

The course is for data analysts or data scientists, software engineers, and developers interested in learning advanced Predictive Analytics with Python. Business analysts/business Intelligence experts who would like to learn how to go from basic predictive models to building advanced models to produce better predictions will also find this course indispensable.
Knowledge of Python and familiarity with its Data Science Stack are assumed. Additionally, an understanding of the basic concepts of predictive analytics and how to use basic predictive models is also necessary to take full advantage of this course.

Target Audiences

The course is for data analysts or data scientists, software engineers, and developers interested in learning advanced Predictive Analytics with Python. Business analysts/business Intelligence experts who would like to learn how to go from basic predictive models to building advanced models to produce better predictions will also find this course indispensable.
Knowledge of Python and familiarity with its Data Science Stack are assumed. Additionally, an understanding of the basic concepts of predictive analytics and how to use basic predictive models is also necessary to take full advantage of this course.

Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems.

When using Predictive Analytics to solve actual problems, besides models and algorithms there are many other practical considerations that must be considered like which features should I use, how many features are enough, should I create new features, how to combine features to give the same underlying information, which hyper-parameters should I use? We explore topics that will help you answer such questions.

Artificial Neural Networks are models loosely based on how neural networks work in a living being. These models have a long history in the Artificial Intelligence community with ups and downs in popularity. Nowadays, because of the increase in computational power, improved methods, and software enhancements, they are popular again and are the basis for advanced approaches such as Deep Learning. This course introduces the use of Deep Learning models for Predictive Analytics using the powerful TensorFlow library.

About the Author :

Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years of 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 in-site) courses to students from around the world in topics like Data Science, Mathematics, Statistics, R programming and Python.

Alvaro Fuentes is a big Python fan and has been working with Python for about 4 years and uses it routinely for analyzing data and producing predictions. He also has used it in a couple of software projects. He is also a big R fan, and doesn’t like the controversy between what is the “best” R or Python, he uses them both. He is also very interested in the Spark approach to Big Data, and likes the way it simplifies complicated
things. He is not a software engineer or a developer but is generally interested in web technologies.

He also has technical skills in R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, mathematical modeling.

Predictive Analytics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice using the Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.

Course Curriculum

Chapter 1: Ensemble Methods for Regression and Classification

Lecture 1: The Course Overview

Lecture 2: How Ensemble Methods Work?

Lecture 3: Bagging, Random Forests, and Boosting for Regression

Lecture 4: Bagging, Random Forests, and Boosting for Classification

Chapter 2: Cross-Validation and Parameter Tuning

Lecture 1: K-fold Cross-Validation

Lecture 2: Comparing Models with K-fold Cross-Validation

Lecture 3: Hyper-Parameter Tuning in scikit-learn

Chapter 3: Working with Features

Lecture 1: Feature Selection Methods

Lecture 2: Dimensionality Reduction and PCA

Lecture 3: Creating New Features

Lecture 4: Improving Models with Feature Engineering

Chapter 4: Introduction to Artificial Neural Networks and TensorFlow

Lecture 1: Introduction to Artificial Neural Networks

Lecture 2: Elements of a Deep Neural Network Model

Lecture 3: Installation and Introduction to TensorFlow

Lecture 4: Core Concepts in TensorFlow

Chapter 5: Predictive Analytics with TensorFlow and Deep Neural Networks

Lecture 1: Predictions with TensorFlow – Introductory Example

Lecture 2: Regression Using Deep Neural Networks

Lecture 3: Classification with Deep Neural Networks

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 3 votes
4 stars: 7 votes
5 stars: 6 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu