Machine Learning Algorithms in Python and R Language
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       Video Length : 25h20m0s
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       Tasks Number : 181
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       Students Enrolled : 1683
  • equalizer
       Medium Level
  • Curriculum
  • 1. Overview the Course
    • videocam
      Introduction to Machine Learning
      10m0s
    • videocam
      Installing Python and Anaconda
      10m0s
    • videocam
      Installing R and R Studio
      10m0s
  • 2. Data Preprocessing
    • videocam
      Preparing the dataset
      10m0s
    • videocam
      Importing the Library
      10m0s
    • videocam
      Importing the Dataset
      10m0s
    • videocam
      Missing Data
      10m0s
    • videocam
      Categorical Data
      10m0s
    • videocam
      Training set and Test set
      10m0s
    • videocam
      Feature Scaling
      10m0s
    • videocam
      Data Preprocessing Template
      10m0s
  • 3. Regression Algorithms
    • videocam
      Get the Dataset for Simple Linear Regression
      10m0s
    • videocam
      Simple Linear Regression Intuition
      10m0s
    • videocam
      Simple Linear Regression in Python
      10m0s
    • videocam
      Simple Linear Regression in R
      10m0s
    • videocam
      Get the dataset for Multiple Linear Regression
      10m0s
    • videocam
      Multiple Linear Regression Intuition
      10m0s
    • videocam
      Multiple Linear Regression in Python
      10m0s
    • videocam
      Backward Elimination in Python
      10m0s
    • videocam
      Multiple Linear Regression in R
      10m0s
    • videocam
      Backward Elimination in R
      10m0s
  • 4. Polynomial Regression Algorithms
    • videocam
      Getting the dataset
      10m0s
    • videocam
      Polynomial Regression in Python
      10m0s
    • videocam
      Regression Template in Python
      10m0s
    • videocam
      Polynomial Regression in R
      10m0s
    • videocam
      Regression Template in R
      10m0s
  • 5. Support Vector Regression
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      SVR Intuition
      10m0s
    • videocam
      SVR in Python
      10m0s
    • videocam
      SVR in R
      10m0s
  • 6. Decision Tree Regression
    • videocam
      Decision Tree Regression Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Decision Tree Regression in Python
      10m0s
    • videocam
      Decision Tree Regression in R
      10m0s
  • 7. Random Forest Regression
    • videocam
      Random Forest Regression Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Random Forest Regression in Python
      10m0s
    • videocam
      Random Forest Regression in R
      10m0s
  • 8. Evaluating Regression Models Performance
    • videocam
      R-Squared Intuition
      10m0s
    • videocam
      Adjusted R-Squared Intuition
      10m0s
    • videocam
      Interpreting Linear Regression Coefficients
      10m0s
  • 9. Logistic Regression Classification
    • videocam
      Logistic Regression Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Logistic Regression in Python
      10m0s
    • videocam
      Classification Template in Python
      10m0s
    • videocam
      Logistic Regression in R
      10m0s
    • videocam
      Classification Template in R
      10m0s
  • 10. K-Nearest Neighbor (K-NN)
    • videocam
      K-Nearest Neighbor Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      K-NN in Python
      10m0s
    • videocam
      K-NN in R
      10m0s
  • 11. Support Vector Machine (SVM)
    • videocam
      SVM Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      SVM in Python
      10m0s
    • videocam
      SVM in R
      10m0s
  • 12. Kernel SVM
    • videocam
      Kernel SVM Intuition
      10m0s
    • videocam
      Mapping to a higher dimension
      10m0s
    • videocam
      The Kernel Trick
      10m0s
    • videocam
      Types of Kernel Functions
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Kernel SVM in Python
      10m0s
    • videocam
      Kernel SVM in R
      10m0s
  • 13. Naive Bayes
    • videocam
      Bayes Theorem
      10m0s
    • videocam
      Naive Bayes Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Naive Bayes in Python
      10m0s
    • videocam
      Naive Bayes in R
      10m0s
  • 14. Decision Tree Classification
    • videocam
      Decision Tree Classification Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Decision Tree Classification in Python
      10m0s
    • videocam
      Decision Tree Classification in R
      10m0s
  • 15. Random Forest Classification
    • videocam
      Random Forest Classification Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Random Forest Classification in Python
      10m0s
    • videocam
      Random Forest Classification in R
      10m0s
  • 16. Evaluating Classification Models Performance
    • videocam
      False Positives & False Negatives
      10m0s
    • videocam
      Confusion Matrix
      10m0s
    • videocam
      Accuracy Paradox
      10m0s
    • videocam
      CAP Curve
      10m0s
    • videocam
      CAP Curve Analysis
      10m0s
  • 17. K-Means Clustering
    • videocam
      K-Means Clustering Intuition
      10m0s
    • videocam
      K-Means Random Initialization Trap
      10m0s
    • videocam
      K-Means Selecting The Number Of Clusters
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      K-Means Clustering in Python
      10m0s
    • videocam
      K-Means Clustering in R
      10m0s
  • 18. Hierarchical Clustering
    • videocam
      Hierarchical Clustering Intuition
      10m0s
    • videocam
      Hierarchical Clustering How Dendrograms Work
      10m0s
    • videocam
      Hierarchical Clustering Using Dendrograms
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Hierarchical Clustering in Python
      10m0s
    • videocam
      Hierarchical Clustering in R
      10m0s
  • 19. Apriori & Eclat Algorithms
    • videocam
      Apriori Intuition
      10m0s
    • videocam
      Apriori the Dataset
      10m0s
    • videocam
      Apriori in R
      10m0s
    • videocam
      Apriori in Python
      10m0s
    • videocam
      Eclat Intuition
      10m0s
    • videocam
      Eclat the Dataset
      10m0s
    • videocam
      Eclat in R
      10m0s
  • 20. Upper Confidence Bound
    • videocam
      The Multi-Armed Bandit Problem
      10m0s
    • videocam
      Upper Confidence Bound (UCB) Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Upper Confidence Bound in Python
      10m0s
    • videocam
      Upper Confidence Bound in R
      10m0s
  • 21. Thompson Sampling
    • videocam
      Thompson Sampling Intuition
      10m0s
    • videocam
      Algorithm Comparison: UCB vs Thompson Sampling
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Thompson Sampling in Python
      10m0s
    • videocam
      Thompson Sampling in R
      10m0s
  • 22. Natural Language Processing
    • videocam
      Natural Language Processing Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Natural Language Processing in Python
      10m0s
    • videocam
      Natural Language Processing in R
      10m0s
  • 23. Artificial Neural Networks
    • videocam
      Introduction to the Neuron
      10m0s
    • videocam
      The Activation Function
      10m0s
    • videocam
      How do Neural Networks work?
      10m0s
    • videocam
      How do Neural Networks learn?
      10m0s
    • videocam
      Gradient Descent
      10m0s
    • videocam
      Stochastic Gradient Descent
      10m0s
    • videocam
      Backpropagation
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Business Problem Description
      10m0s
    • videocam
      ANN in Python
      10m0s
    • videocam
      ANN in R
      10m0s
  • 24. Convolutional Neural Networks
    • videocam
      Introduction to CNN
      10m0s
    • videocam
      Convolution Operation
      10m0s
    • videocam
      ReLU Layer
      10m0s
    • videocam
      Pooling
      10m0s
    • videocam
      Flattening
      10m0s
    • videocam
      Full Connection
      10m0s
    • videocam
      Softmax & Cross-Entropy
      10m0s
    • videocam
      CNN in Python
      10m0s
    • videocam
      CNN in R
      10m0s
  • 25. Principal Component Analysis
    • videocam
      Principal Component Analysis (PCA) Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      PCA in Python
      10m0s
    • videocam
      PCA in R
      10m0s
  • 26. Linear Discriminant Analysis
    • videocam
      Linear Discriminant Analysis (LDA) Intuition
      10m0s
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      LDA in Python
      10m0s
    • videocam
      LDA in R
      10m0s
  • 27. Kernel PCA
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      Kernel PCA in Python
      10m0s
    • videocam
      Kernel PCA in R
      10m0s
  • 28. Model Selection
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      k-Fold Cross Validation in Python
      10m0s
    • videocam
      k-Fold Cross Validation in R
      10m0s
    • videocam
      Grid Search in Python
      10m0s
    • videocam
      Grid Search in R
      10m0s
  • 29. XGBoost
    • videocam
      Getting the Dataset
      10m0s
    • videocam
      XGBoost in Python
      10m0s
    • videocam
      XGBoost in R
      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...

Machine Learning Algorithms in Python and R Language


Machine Learning Algorithms in Python and R Language

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