- Chia sẻ kiến thức hữu ích!

Chia sẻ Khóa Học Dự Án Thực Trong Machine Learning : Từ Cơ Bản Đến Chuyên Gia

Tìm hiểu các khái niệm cốt lõi của Machine Learning
Tìm hiểu về các loại thuật toán học máy khác nhau
Xây dựng các dự án thế giới thực bằng thuật toán học được giám sát và không giám sát
Tìm hiểu cách triển khai mạng nơron

9 dự án thực hiện trong khóa học này:

Project 1 -Board Game Review Prediction – In this project, you’ll see how to perform a linear regression analysis by predicting the average reviews on a board game in this project.
Project 2 – Credit Card Fraud Detection – In this project, you’ll learn to focus on anomaly detection by using probability densities to detect credit card fraud.
Project 3 – Stock Market Clustering – Learn how to use the K-means clustering algorithm to find related companies by finding correlations among stock market movements over a given time span.
Project 4 – Getting Started with Natural Language Processing In Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.

Project 5– Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a benchmark to implement a recently published deep neural network.

Project 6 – Image Super Resolution with the SRCNN – Learn how to implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for improving image quality.
Project 7 – Natural Language Processing: Text Classification – In this project, you’ll learn an advanced approach to Natural Language

Processing by solving a text classification task using multiple classification algorithms.
Project 8 – K-Means Clustering For Image Analysis – In this project, you’ll learn how to use K-Means clustering in an unsupervised

learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset.
Project 9 – Data Compression & Visualization Using Principle Component Analysis – This project will show you how to compress

our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering.