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Google Certified Data Engineer Exam Simulator

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Imbalanced Classification Master Class in Python

This course was designed around major imbalanced classification techniques that are directly relevant to real-world problems. This is a master class on handling real-world class imablance.

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Fast Track Introduction to Python for Machine Learning Engineers

Python has become the gold standard in applied machine learning. This course will provide you with a complete foundation in Python. The course was created for machine learning engineers by machine learning engineers.

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Applied Linear Algebra for Machine Learning Engineers

Linear algebra is the mathematics of data. It’s all vectors and matrices of numbers. Rather than starting with theorems and abstract concepts, you can learn the basics of linear algebra in a concrete way with examples. That's what this is about.

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Building Machine Learning Algorithms in Python from Scratch

This is your guide to learning the details of machine learning algorithms by implementing them from scratch in Python. You will discover how to load data, evaluate models and implement a suite of top machine learning algorithms.

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The Google Certified Data Engineer

This course is a complete end-to-end solution for preparing for and passing the Google Certified Data Engineer.

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Microsoft Certified: Data Analyst Associate

This course will help you prepare for the Microsoft Certified Data Analyst Associate. You'll learn data analysis and then pass exam DA-100.

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Applied Machine Learning with BigQuery on Google's Cloud Platform

Google Cloud Platform's BigQuery is a serverless, petabyte-scale data warehouse designed to house structured datasets and enable lightning fast SQL queries.

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Data Cleansing Master Class in Python

Data preparation may be the most important part of a machine learning project. It is the most time consuming part, although it seems to be the least discussed topic. Learn data cleansing from start to finish.

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Feature Engineering Case Study in Python

The quality of the predictions coming out of your machine learning model is a direct reflection of the data you feed it during training. This course provides the tools to take a data set and throw out the noise for modeling success.

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Avoiding Pitfalls in the Machine Learning Process

Building machine learning models can be an exciting process. But oftentimes, machine learning engineers find themselves dealing with errors, bad output, and a host of other issues that can slow their progress.

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Performance Tuning Deep Learning Models Masterclass

This course was designed around three main activities for getting better results with deep learning models: better or faster learning, better generalization to new data, and better predictions when using final models.

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Hands-On Keras for Machine Learning Engineers

Welcome to Hands-On Keras for Machine Learning Engineers. This course is your guide to deep learning in Python with Keras. You will discover the Keras Python library for deep learning and how to use it to develop and evaluate deep learning models.

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Applied Statistics for Machine Learning Engineers

In this course you'll learn applied statistics for machine learning. The course will focus on the knowledge of statistics you need for your machine learning projects.  You'll be able to take what you've learned and apply it to your real-world problem

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A Complete Introduction to XGBoost

In competitive modeling and the real world, a group of algorithms known as gradient boosters has taken the world be storm.  They've won almost every single competition in the structured data category.  XGBoost is the king of these models.

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An Introduction to Data Cleansing in Python

A recent study from Kaggle determined that 80% of time data scientists and machine learning engineers spend their time cleaning data. This course is all about how to clean your data.

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Python for Machine Learning Engineers

In this course we are going to learn Python using a lab integrated approach. Programming is something you have to do in order to master it. You can't read about Python and expect to learn it. 

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SciKit-Learn in Python for Machine Learning Engineers

If you take this course from start to finish you'll know the core foundations of a machine learning library in Python called SciKit-Learn. This course is centered around building traditional machine learning models in SciKit-Learn.

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Data Visualization in Python for Machine Learning Engineers

In this course you are going to focus on data visualization and in Python that means we you going to be learning matplotlib and seaborn. Matplotlib is a Python package for 2D plotting that generates production-quality graphs.

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Acing the Machine Learning Engineering Interview

If you want to land a job as a machine learning engineer, you’ll need to pass a rigorous and competitive interview process. Most top companies will have a phone screen and at least two to three rounds of in-person interviews. 

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Natural Language Processing with Deep Learning Master Class

Natural language processing is the area of study dedicated to the automatic manipulation of speech and text by software. This course is a hands on-guide to applying deep learning to NLP.

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Starting You Career on LogikBot

Welcome to Logicbot. This brief course will provide you with a guide to your journey as a machine learning engineer.

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The Fundamentals of Transact-SQL for Data Professionals

Microsoft’s flavor of SQL is called Transact-SQL. Transact was designed primarily to work with relational databases. Transact is the language used to talk to SQL Server.