This is the code repository for Machine Learning with C++ [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
ML has become a fundamental part of the 21st century; from Netflix recommendations to fraud detection, ML is ever- present in our daily lives. At its roots, ML effectively applies statistics and pattern recognition, we will use these ideas to help solve a range of modern-day problems. C++ is a very fast language to execute your code and is extensively used when your final “models” are being deployed. If you want to run a program, with a lot of array calculation then C++ should be your weapon of choice.
This course will start off with a broad overview of ML and the varying methods associated with it. You will understand data types, Machine Learning algorithms, and a simple classification task. We then study two simple but effective algorithms to deepen your understanding and provide some practical experience. Specifically, the two algorithms that we will be investigating are linear regression and K-means clustering.
By taking this course, you will be able to get your machine Learning basics right and be able to build efficient algorithms which will help you to predict and cluster data.
- Start your Machine Learning journey with C++
- Understand the difference between generative and discriminative Machine Learning.
- Understand the difference between unsupervised and supervised learning.
- Explore the benefits of Linear Regression and Logistic Regression.
- Implement a Linear Regression algorithm.
- Understand the difference between K-means and K-NN.
- Implement a K-means algorithm.
To fully benefit from the coverage included in this course, you will need:
NA
This course has the following software requirements:
NA
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Machine Learning with C++ : Choosing the Right Algorithm [Video]
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Implementing Deep Learning Algorithms with TensorFlow 2.0 [Video]
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