پیشنهاد فرادرس

آموزش یادگیری ماشینی با پایتون در 7 روز

دسته بندی ها: آموزش پایتون (Python) ، آموزش های Packtpub ، یادگیری عمیق (Deep Learning) ، هوش مصنوعی ، یادگیری ماشینی (Machine Learning)

یادگیری ماشینی یکی از مهارت های مورد نیاز در بازار است. اما آیا تا به حال فکر کرده اید از کجا شروع کنید؟ در این دوره با یادگیری استفاده از یادگیری ماشینی در کمتر از یک هفته، معرفی یک جنبه ی یادگیری ماشین جدید در هر بخش، رویکرد سیستماتیک و سریع، یادگیری ماشین با استفاده از پایتون، توسعه پروژه های یادگیری ماشین در پایتون در یک هفته و غیره آشنا می شوید.

سرفصل:

  • معرفی دوره
  • راه اندازی محیط یادگیری ماشینی
  • انواع یادگیری ماشینی
  • استفاده از Scikit-learn برای یادگیری ماشینی
  • ساخت اولین مدل پیش بینی شما
  • الگوریتم یادگیری تحت نظارت
  • معماری یک سیستم یادگیری ماشینی
  • مدل یادگیری ماشینی و کامپوننت های آن
  • رگرسیون خطی
  • طبقه بندی تصویر با استفاده از یادگیری تحت نظارت
  • رگرسیون منطقی
  • Kernels در SVM
  • ارزیابی مدل
  • یادگیری بدون نظارت
  • Clustering
  • K-means Clustering
  • درختان تصمیم گیری
  • و غیره
به این نوشته امتیاز دهید 1 2 3 4 5 بدون امتیاز
Python Machine Learning in 7 Days [Video] Publisher:Packtpub Author:Arish Ali Duration:2 hours 22 minutes

Build powerful Machine Learning models using Python with hands-on practical examples in just a week.
Machine learning is one of the most sought-after skills in the market. But have you ever wondered where to start or found the course not so easy to follow. With this hands-on and practical machine learning course, you can learn and start applying machine learning in less than a week without having to be an expert mathematician.
In this course, you will be introduced to a new machine learning aspect in each section followed by a practical assignment as a homework to help you in efficiently implement the learnings in a practical manner. With the systematic and fast-paced approach to this course, learn machine learning using Python in the most practical and structured way to develop machine learning projects in Python in a week.
This course is structured to unlock the potential of Python machine learning in the shortest amount of time. If you are looking to upgrade your machine learning skills using Python in the quickest possible time, then this course is for you!
Style and Approach
This is a fast-paced course offering practical and actionable guidance with step-by-step instruction and assignments. This course will enable you to develop your own ML models and methods to use them efficiently in the quickest possible way.
Released: Monday, June 25, 2018
Enter the Machine Learning World!
The Course Overview
Setting Up Your Machine Learning Environment
Exploring Types of Machine Learning
Using Scikit-learn for Machine Learning
Assignment – Train Your First Pre-built Machine Learning Model
Build Your First Predicting Model
Supervised Learning Algorithm
Architecture of a Machine Learning System
Machine Learning Model and Its Components
Linear Regression
Predicting Weight Using Linear Regression
Assignment – Predicting Energy Output of a Power Plant
Image Classification Using Supervised Learning
Review of Predicting Energy Output of a Power Plant
Logistic Regression
Classifying Images Using Logistic Regression
Support Vector Machines
Kernels in a SVM
Classifying Images Using Support Vector Machines
Assignment – Start Image Classifying Using Support Vector Machines
Improving Model Accuracy
Review of Classifying Images Using Support Vector Machines
Model Evaluation
Better Measures than Accuracy
Understanding the Results
Improving the Models
Assignment – Getting Better Test Sample Results by Measuring Model Performance
Finding Patterns and Structures in Unlabeled Data
Review of Getting Better Test Sample Results by Measuring Model Performance
Unsupervised Learning
Clustering
K-means Clustering
Determining the Number of Clusters
Assignment – Write Your Own Clustering Implementation for Customer Segmentation
Sentiment Analysis Using Neural Networks
Review of Clustering Customers Together
Why Neural Network
Parts of a Neural Network
Working of a Neural Network
Improving the Network
Assignment – Build a Sentiment Analyzer Based on Social Network Using ANN
Mastering Kaggle Titanic Competition Using Random Forest
Review of Building a Sentiment Analyser ANN
Decision Trees
Working of a Decision Tree
Techniques to Further Improve a Model
Random Forest as an Improved Machine Learning Approach
Weekend Task – Solving Titanic Problem Using Random Forest

پیشنهاد فرادرس

لینک های دانلود حجم فایل: 302.0MB Packtpub Python Machine Learning in 7 Days [Video]_git.ir.rar