English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 29m | 320 MB
Up your algorithm building game by using C++ to predict and cluster data.
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.
This course takes you through the fundamentals of Machine Learning, and how you can utilize your C++ skills to build efficient algorithms for predicting and clustering data.
What You Will Learn
- 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.
Table of Contents
The Purposes of Machine Learning
01 The Course Overview
02 Why Do We Use ML
03 Variety of Data Types
04 Variety of ML Algorithms
05 Simple Classification Task
06 Why Choose C++
Modeling a Problem with Linear Regression
07 A Brief Overview of Linear Regression
08 Implementing Linear Regression
09 Implementing the trainAlgorithm Member Function
10 Implementing the Regress Function
11 Implementing Linear Regression Using a while Loop
12 Adjusting the Step Size
13 Discussion
Cluster Analysis with K-Means
13 Discussion
14 What Is Clustering
15 Implementing the K-Means Algorithm
16 Implementing the clusterData Function
17 Implementing computeMeans Function
18 Implementing the assignLabels Function
19 Implementing the printClusters Function
20 Choosing K
Resolve the captcha to access the links!