DataCamp#1 [OnlineCamp] !!! Out Of Stock !!!



ค่าเรียน 32,900 บาท
(เรียนจบรับเงินคืนสูงสุด 12,900 บาท หากหางานกับ Camp รับเงินโบนัส 20,000 บาท ค่าเรียนจริงฟรี )

คอร์สเรียน ONLINE

    • ระยะเวลาเรียนทั้งหมด 6 เดือน (ควรจะมีเวลาเรียนอย่างน้อยสัปดาห์ละ 40 ชม.)
    • เรียนที่ไหน เวลาไหนก็ได้
    • มีผู้สอน ช่วยเหลือตลอด 9.00-21.00

Demo Day เลือกบริษัทที่ใช่กว่า 50 บริษัท

  • วันสุดท้ายหลังจบคอร์ส เราจะเชิญบริษัทมาร่วมชมผลงานจากการฝึกงานของคุณ และพร้อมให้คุณเลือกทำงานทันทีหลังจบแคมป์!
  • Career Consult ตลอดระยะเวลาการเรียน เพื่อหางานที่เหมาะสมกับคุณ

*** เงื่อนไขการคืนเงิน ***

  1. เมื่อเรียนจบจะได้รับเงินคืนตั้งแต่ 6,900 บาท สูงสุด 12,900 บาท ตามราคาค่าเรียนในเวลานั้นๆ
  2. เงินคืนจะได้รับเมื่อเรียนจบตามหลักสูตรแล้วเท่านั้น
  3. หากหางานกับโครงการเมื่อเรียนจบ จะได้รับเงินโบนัสคืนจำนวน 20,000 บาท
  4. โครงการขอสงวนสิทธิ์ในการแก้ไข เปลี่ยนแปลง หรือยกเลิกเงื่อนไข โดยไม่ต้องแจ้งให้ทราบล่วงหน้า

Course Syllabus 

Week 1

  • Foundations Review
    Git with Teams
  • Linux system
    o Operating Systems and Linux
    o File System and File Operations
    o Text-processing commands
    o Other useful commands
  •  SQL
    o Intro to SQL
    o Tables and schemas
    o SQL queries – SELECT
    o MySQL database management
    o Joins
  • Python Basic ทบทวน
  • Project – Simple Python Data Analysis
  • Project – Simple Python Data Analysis

Week 2

  • Advanced Topics
    o Multiple-list operations: map and zip
    o Functional operators: reduce
    o Object Oriented Programming
  • Advanced Topics
    o Multiple-list operations: map and zip
    o Functional operators: reduce
    o Object Oriented Programming
  • Introduction to Web Scraping
    o Regular Expressions
    o Introduction to HTML
    o Basics of Beautifulsoup
    o Examples”
    Introduction to Scrapy
    o An example
    o Getting Started
    o Items/spider/pipelines/settings.py
    o In Class Lab
  • Introduction to Pandas
    o Data Structure
    o Data Manipulation
    o Handling missing data
    o Grouping and aggregationProject Day: Web Scraping
  • Matplotlib & Seaborn
    o In-class Lab”Missingness & Imputation

    o Missing Data
    o Basic Methods of Imputation
    o K-Nearest Neighbors
    o Review

Week 3

  • Linear Regression I
    o Simple Linear Regression
    o Assumptions & Diagnostics
    o Transformations
    o The Coefficient of Determination
  • Linear Regression II
    o Multiple Linear Regression
    o Assumptions & Diagnostics
    o Research Questions of Interest
    o Extending Model Flexibility
    o Review
  • Generalized Linear Models
    o Logistic Regression
    o Maximum Likelihood Estimation
    o Model Interpretation
    o Assessing Model Fit
    o Review
  • The Curse of Dimensionality
    o Ridge Regression
    o Lasso Regression
    o Cross-Validation
    o Bias/Variance Tradeoff
  • Project : Simple ML with Python

Week 4

  • Tree Methods I
    o Decision Trees
    o Bagging
    o Random Forest
    o Boosting
    o Variable Importance
  • Tree Methods II
    o Decision Trees
    o Bagging
    o Random Forest
    o Boosting
    o Variable Importance
  • Support Vector Machines
    o Maximal Margin Classifier
    o Support Vector Classifier
    o Support Vector Machines
    o Multi-Class SVMs
    o Review
  • Association Rules & Naïve Bayes
    o Association Rule Mining
    o Naïve Bayes
    o Review
  • Python – Linear Regression
    o What is Machine Learning
    Introduction to Scikit-Learn
    o Simple Linear Regression
    o Multiple Linear Regression
    o StatsmodelsProject : Advance Prediction

Week 5

  • Python – Classification Part I
    o Limitation of Linear Regression
    o Logistic Regression
    o Discriminant Analysis: Motivation
    o Discriminant Analysis: Models
    o Naïve Bayes
  • Python – Model Selection
    o Cross-Validation
    o Bootstrap
    o Feature Selection
    o Regularization
    o Grid Search
  • Principal Component Analysis
    o Taking a New Perspective
    o Dimension Reduction
    o Vectors of Highest Variance
    o The PCA Procedure
  • Cluster Analysis
    o Intro to Cluster Analysis
    o K-Means Clustering
    o Hierarchical Clustering
    o Clustering Takeaways
    o Review
  • Python – Unsupervised Learning
    o Intro to Unsupervised Learning
    o Principal Comp
    Python – Advanced Regression
    • Python – Advanced ClassificationProject : Advance Prediction 2

Week 6

  • How Deep Learning Works
    o Neural Units
    o Neurons in TensorFlow
    o Cost Functions, Gradient Descent, and Backpropagation
    o Fitting Models in TensorFlow
    o Interactive Visualization of a Deep Neural Network
    o TensorBoard and Interpretation
  • TensorFlow Lab
    o Random Initialization and Stochastic Gradient Descent
    o Introduction to Convolutional Neural Networks for Visual Recognition
    o Dropout and Regularization
    o Tuning Hyperparameters
  • Machine Vision
    o Classic ConvNet Architecture I: LeNet-5
    o Classic ConvNet Architecture II: AlexNet
    o Classic ConvNet Architecture II: VGGNet
    o Transfer Learning
    o Dogs vs Cats Kaggle Competition
  • Natural Language Processing
    o Word Vectors: word2vec and Vector-Space Embedding
    o Build a recommendation system with doc2vec
    o Sentiment Analysis using Convolutional Neural Network
  • Time Series Analysis
    o The Nature of Time Series Analysis
    o Learn from the Examples
    o Decomposition of Time Series Data
    o Examples of Stationary Non-White-Noise Time Series
    o ARMA and ARIMA Models
    o Assessing Model Fit
    Project : Final Project (เริ่มทำ)

Week 7

  • Database Management Tools
    o AWS cloud services (IAM, S3, EC2, RDS.)
    o MySQL / AWS RDS
    o GUI Tool: MySQLWorkBench
    o MySQL Python Connector
  • NoSQL Databases and MongoDB
    o Intro to NoSQL
    o Installing MongoDB on AWS EC2
    o Common database commands
    o GUI tool: MongoDB Compass
    o pyMongo
  • Time Series Analysis with Deep Learning
    o Recurrent Neural Networks
    o Long Short-Term Memory Units
    o Forecasting with Financial Time Series Data
    o Web Traffic Time Series Forecasting Kaggle 1st Place Solution
  • Reinforcement Learning
    o Applications of Reinforcement Learning
    o Essential Theory of Reinforcement Learning
    o OpenAI Gym
    o Two Sigma Halite Competition
  • Project : Final Project (continue)

Week 8

  •  A/B Testing
    • Capstone Project Presentations
    • Machine Learning Theory Defense
    • SQL Code Challenge
  • Project : Final Project (continue)

Week 9

  • Project : Final Project (continue)

Week 10

  • Project : Final Project (continue)
  • Expert Lecture Series
  • How to do a phone interview
    How to do an in-person interview
    How to write a resume
    Writing a cover letter
    Where to find tech jobs
  • Presentation Final Project

Topics for this course

160 Lessons

Python Crash Course Part 1

Welcome to Python Programming2:13:51

Python Crash Course Part 2





Interactive Visualization: Bokeh, Plotly

PROJECT I – Capstone






Stat 1

Stat 2

Introduction to Machine Learning

Linear Regression

Support Vector Regression (SVR)

Logistic Regression

K-Nearest Neighbors (K-NN)

Support Vector Machine (SVM)

Naive Bayes

Decision Tree

Random Forest

Regression Project

PROJECT III – Classification (Filter Spam Email – Naive Bayes Classifier)

Principal Component Analysis PCA

Linear Discriminant Analysis (LDA)

Dimensionality Reduction Project

K-Means Clustering

Hierarchical Clustering

PROJECT V – Clustering

Basic Natural Language Processing

PROJECT VI – Natural Language Processing

Deep Learning

Deep Learning With Tensorflow + Beautiful Soup


Course Details

  • Level: Expert
  • Categories: ONLINE COURSE
  • Total Lesson: 160
  • Total Enrolled: 103
  • Last Update: 1 October 2020
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