The Complete Machine Learning Course with Python
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       Video Length : 16h20m0s
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       Tasks Number : 108
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       Students Enrolled : 1031
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       Medium Level
  • Curriculum
  • 1. Introduction
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      What Does the Course Cover?
      10m0s
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      How to Succeed in This Course
      10m0s
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      Project Files
      10m0s
  • 2. Getting Started with Anaconda
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      [Windows OS] Downloading & Installing Anaconda
      10m0s
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      [Windows OS] Managing Environment
      10m0s
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      [Mac OS] Intructions on Installing Anaconda and Managing Environment
      10m0s
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      Practice Activity: Create a New Environment
      10m0s
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      Navigating the Spyder & Jupyter Notebook Interface
      10m0s
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      Downloading the IRIS Datasets
      10m0s
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      Data Exploration and Analysis
      10m0s
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      Presenting Your Data
      10m0s
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      Getting Started
      10m0s
  • 3. Regression
    • videocam
      Introduction
      10m0s
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      Categories of Machine Learning
      10m0s
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      Machine Learning Basic Concepts
      10m0s
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      Working with Scikit-Learn
      10m0s
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      Boston Housing Data - EDA
      10m0s
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      Correlation Analysis and Feature Selection
      10m0s
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      Simple Linear Regression Modelling with Boston Housing Data
      10m0s
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      Robust Regression
      10m0s
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      Evaluate Model Performance
      10m0s
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      Multiple Regression with statsmodel
      10m0s
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      Multiple Regression and Feature Importance
      10m0s
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      Ordinary Least Square Regression and Gradient Descent
      10m0s
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      Regularised Method for Regression
      10m0s
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      Polynomial Regression
      10m0s
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      Dealing with Non-linear relationships
      10m0s
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      Feature Importance Revisited
      10m0s
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      Data Pre-Processing 1
      10m0s
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      Data Pre-Processing 2
      10m0s
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      Variance Bias Trade Off - Validation Curve
      10m0s
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      Variance Bias Trade Off - Learning Curve
      10m0s
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      Cross Validation
      10m0s
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      Section 3
      10m0s
  • 4. Classification
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      Introduction
      10m0s
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      Logistic Regression 1
      10m0s
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      Logistic Regression 2
      10m0s
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      MNIST Project 1 - Introduction
      10m0s
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      MNIST Project 2 - SGDClassifier
      10m0s
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      MNIST Project 3 - Performance Measures
      10m0s
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      MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score
      10m0s
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      MNIST Project 5 - Precision and Recall Tradeoff
      10m0s
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      MNIST Project 6 - The ROC Curve
      10m0s
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      MNIST Exercise
      10m0s
  • 5. Support Vector Machine (SVM)
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      Introduction
      10m0s
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      Support Vector Machine (SVM) Concepts
      10m0s
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      Linear SVM Classification
      10m0s
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      Polynomial Kernel
      10m0s
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      Gaussian Radial Basis Function
      10m0s
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      Support Vector Regression
      10m0s
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      Advantages and Disadvantages of SVM
      10m0s
  • 6. Tree Decision
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      Introduction
      10m0s
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      What is Decision Tree
      10m0s
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      Training a Decision Tree
      10m0s
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      Visualising a Decision Trees
      10m0s
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      Decision Tree Learning Algorithm
      10m0s
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      Decision Tree Regression
      10m0s
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      Overfitting and Grid Search
      10m0s
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      Where to From Here
      10m0s
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      Project HR - Loading and preprocesing data
      10m0s
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      Project HR - Modelling
      10m0s
  • 7. Ensemble Machine Learning
    • videocam
      Introduction
      10m0s
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      Ensemble Learning Methods Introduction
      10m0s
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      Bagging Part 1
      10m0s
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      Bagging Part 2
      10m0s
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      Random Forests
      10m0s
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      Extra-Trees
      10m0s
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      AdaBoost
      10m0s
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      Gradient Boosting Machine
      10m0s
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      XGBoost
      10m0s
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      Project HR - Human Resources Analytics
      10m0s
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      Ensemble of ensembles Part 1
      10m0s
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      Ensemble of ensembles Part 2
      10m0s
  • 8. k-Nearest Neighbours (kNN)
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      kNN Introduction
      10m0s
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      kNN Concepts
      10m0s
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      kNN and Iris Dataset Demo
      10m0s
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      Distance Metric
      10m0s
    • videocam
      Project Cancer Detection Part 1
      10m0s
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      Project Cancer Detection Part 2
      10m0s
  • 9. Dimensionality Reduction
    • videocam
      Introduction
      10m0s
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      Dimensionality Reduction Concept
      10m0s
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      PCA Introduction
      10m0s
    • videocam
      Dimensionality Reduction Demo
      10m0s
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      Project Wine 1: Dimensionality Reduction with PCA
      10m0s
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      Project Abalone
      10m0s
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      Project Wine 2: Choosing the Number of Components
      10m0s
    • videocam
      Kernel PCA
      10m0s
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      Kernel PCA Demo
      10m0s
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      LDA & Comparison between LDA and PCA
      10m0s
  • 10. Unsupervised Learning: Clustering
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      Introduction
      10m0s
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      Clustering Concepts
      10m0s
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      MLextend
      10m0s
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      Ward’s Agglomerative Hierarchical Clustering
      10m0s
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      Truncating Dendrogram
      10m0s
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      k-Means Clustering
      10m0s
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      Elbow Method
      10m0s
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      Silhouette Analysis
      10m0s
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      Mean Shift
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

The Complete Machine Learning Course with Python


The Complete Machine Learning Course with Python

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