Udemy

SGLearn@Python for Data Science & Machine Learning Bootcamp

立即報名
  • 22 名學生
  • 更新於 6/2017
4.3
(07 個評分)
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課程資料

報名日期
全年招生
課程級別
學習模式
修業期
21 小時 52 分鐘
教學語言
英語
授課導師
DioPACT SG
評分
4.3
(07 個評分)
1次瀏覽

課程簡介

SGLearn@Python for Data Science & Machine Learning Bootcamp

This is an Adapted Course for Singaporeans picking up new skillsets and competencies under the CITREP+ Scheme.

Welcome to the SGLearn Series targeted at Singapore-based learners picking up new skillsets and competencies.

This course is an adaptation of the same course by Jose Marcial Portilla and is specially produced in collaboration with Jose for Singaporean learners. If you are a Singaporean, you are eligible for the CITREP+ funding scheme, terms and conditions apply.

---------------

Note from Jose ....

Are you ready to start your path to becoming a Data Scientist!

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

  • Programming with Python

  • NumPy with Python

  • Using pandas Data Frames to solve complex tasks

  • Use pandas to handle Excel Files

  • Web scraping with python

  • Connect Python to SQL

  • Use matplotlib and seaborn for data visualizations

  • Use plotly for interactive visualizations

  • Machine Learning with SciKit Learn, including:

  • Linear Regression

  • K Nearest Neighbors

  • K Means Clustering

  • Decision Trees

  • Random Forests

  • Natural Language Processing

  • Neural Nets and Deep Learning

  • Support Vector Machines

  • and much, much more!

Enroll in the course and become a data scientist today!


課程章節

  • 28 個章節
  • 144 堂課
  • 第 1 章 Course Introduction
  • 第 2 章 Environment Set-Up
  • 第 3 章 Jupyter Overview
  • 第 4 章 Python Crash Course
  • 第 5 章 Python for Data Analysis - NumPy
  • 第 6 章 Python for Data Analysis - Pandas
  • 第 7 章 Python for Data Analysis - Pandas Exercises
  • 第 8 章 Python for Data Visualization - Matplotlib
  • 第 9 章 Python for Data Visualization - Seaborn
  • 第 10 章 Python for Data Visualization - Pandas Built-in Data Visualization
  • 第 11 章 Python for Data Visualization - Plotly and Cufflinks
  • 第 12 章 Python for Data Visualization - Geographical Plotting
  • 第 13 章 Data Capstone Project
  • 第 14 章 Introduction to Machine Learning
  • 第 15 章 Linear Regression
  • 第 16 章 Cross Validation and Bias-Variance Trade-Off
  • 第 17 章 Logistic Regression
  • 第 18 章 K Nearest Neighbors
  • 第 19 章 Decision Trees and Random Forests
  • 第 20 章 Support Vector Machines
  • 第 21 章 K Means Clustering
  • 第 22 章 Principal Component Analysis
  • 第 23 章 Recommender Systems
  • 第 24 章 Natural Language Processing
  • 第 25 章 Big Data and Spark with Python
  • 第 26 章 Neural Nets and Deep Learning
  • 第 27 章 BONUS: DISCOUNT COUPONS FOR OTHER COURSES
  • 第 28 章 Interview with Singapore Expert

課程內容

  • Use Python for Data Science and Machine Learning, Use Spark for Big Data Analysis, Implement Machine Learning Algorithms


評價

  • I
    IMDA Panel Access
    5.0

    The materials are comprehensive and are properly organized. It is truly insightful and give whoever want to learn data science a leg up to embark on this area. Many thanks for the excellent effort.

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