Machine Learning with Python, scikit-learn and TensorFlow
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       Video Length : 18h30m0s
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       Tasks Number : 114
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       Students Enrolled : 753
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       Medium Level
  • Curriculum
  • 1. Step-by-Step Machine Learning with Python
    • videocam
      The Course Overview
      10m0s
    • videocam
      Introduction to Machine Learning
      10m0s
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      Installing Software and Setting Up
      10m0s
    • videocam
      Understanding NLP
      10m0s
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      Touring Powerful NLP Libraries in Python
      10m0s
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      Getting the Newsgroups Data
      10m0s
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      Thinking about Features
      10m0s
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      Visualization
      10m0s
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      Data Preprocessing
      10m0s
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      Clustering
      10m0s
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      Topic Modeling
      10m0s
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      Getting Started with Classification
      10m0s
    • videocam
      Exploring Naïve Bayes
      10m0s
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      The Mechanics of Naïve Bayes
      10m0s
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      The Naïve Bayes Implementation
      10m0s
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      Classifier Performance Evaluation
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      Model Tuning and cross-validation
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      Recap and Inverse Document Frequency
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      The Mechanics of SVM
      10m0s
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      The Implementations of SVM
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      The Kernels of SVM
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      Choosing Between the Linear and the RBF Kernel
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      News topic Classification with Support Vector Machine
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      Fetal State Classification with SVM
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      Brief Overview of Advertising Click-Through Prediction
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      Decision Tree Classifier
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      The Implementations of Decision Tree
      10m0s
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      Click-Through Prediction with Decision Tree
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      Random Forest - Feature Bagging of Decision Tree
      10m0s
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      One-Hot Encoding - Converting Categorical Features to Numerical
      10m0s
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      Logistic Regression Classifier
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      Click-Through Prediction with Logistic Regression by Gradient Descent
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      Feature Selection via Random Forest
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      Brief Overview of the Stock Market And Stock Price
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      Predicting Stock Price with Regression Algorithms
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      Data Acquisition and Feature Generation
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      Linear Regression
      10m0s
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      Decision Tree Regression
      10m0s
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      Support Vector Regression
      10m0s
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      Regression Performance Evaluation
      10m0s
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      Stock Price Prediction with Regression Algorithms
      10m0s
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      Best Practices in Data Preparation Stage
      10m0s
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      Best Practices in the Training Sets Generation Stage
      10m0s
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      Best Practices in the Model Training, Evaluation, and Selection Stage
      10m0s
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      Best Practices in the Deployment and Monitoring Stage
      10m0s
  • 2. Machine Learning with Scikit-learn
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      The Course Overview
      10m0s
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      Defining Machine Learning
      10m0s
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      Training Data, Testing Data, and Validation Data
      10m0s
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      Bias and Variance
      10m0s
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      An Introduction to Scikit-learn
      10m0s
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      Installing Pandas, Pillow, NLTK, and Matplotlib
      10m0s
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      What Is Simple Linear Regression?
      10m0s
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      Evaluating the Model
      10m0s
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      KNN, Lazy Learning, and Non-Parametric Models
      10m0s
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      Classification with KNN
      10m0s
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      Regression with KNN
      10m0s
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      Extracting Features from Categorical Variables
      10m0s
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      Standardizing Features
      10m0s
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      Extracting Features from Text
      10m0s
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      Multiple Linear Regression
      10m0s
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      Polynomial Regression
      10m0s
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      Regularization
      10m0s
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      Applying Linear Regression
      10m0s
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      Gradient Descent
      10m0s
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      Binary Classification with Logistic Regression
      10m0s
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      Spam Filtering
      10m0s
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      Tuning Models with Grid Search
      10m0s
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      Multi-Class Classification
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      Multi-Label Classification and Problem Transformation
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      Bayes' Theorem
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      Generative and Discriminative Models
      10m0s
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      Naive Bayes with Scikit-learn
      10m0s
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      Decision Trees
      10m0s
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      Training Decision Trees
      10m0s
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      Decision Trees with Scikit-learn
      10m0s
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      Bagging
      10m0s
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      Boosting
      10m0s
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      Stacking
      10m0s
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      The Perceptron–Basics
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      Limitations of the Perceptron
      10m0s
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      Kernels and the Kernel Trick
      10m0s
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      Maximum Margin Classification and Support Vectors
      10m0s
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      Classifying Characters in Scikit-learn
      10m0s
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      Nonlinear Decision Boundaries
      10m0s
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      Feed-Forward and Feedback ANNs
      10m0s
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      Multi-Layer Perceptrons and Training Them
      10m0s
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      Clustering
      10m0s
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      K-means
      10m0s
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      Evaluating Clusters
      10m0s
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      Image Quantization
      10m0s
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      Principal Component Analysis
      10m0s
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      Visualizing High-Dimensional Data and Face Recognition with PCA
      10m0s
  • 3. Machine Learning with TensorFlow
    • videocam
      The Course Overview
      10m0s
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      Introducing Deep Learning
      10m0s
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      Installing TensorFlow on Mac OSX
      10m0s
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      Installation on Windows – Pre-Reqeusite Virtual Machine Setup
      10m0s
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      Installation on Windows/Linux
      10m0s
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      The Hand-Written Letters Dataset
      10m0s
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      Automating Data Preparation
      10m0s
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      Understanding Matrix Conversions
      10m0s
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      The Machine Learning Life Cycle
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      Reviewing Outputs and Results
      10m0s
    • videocam
      Getting Started with TensorBoard
      10m0s
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      TensorBoard Events and Histograms
      10m0s
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      The Graph Explorer
      10m0s
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      Our Previous Project on TensorBoard
      10m0s
    • videocam
      Fully Connected Neural Networks
      10m0s
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      Convolutional Neural Networks
      10m0s
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      Programming a CNN
      10m0s
    • videocam
      Using TensorBoard on Our CNN
      10m0s
    • videocam
      CNN Versus Fully Connected Network Performance
      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 with Python, scikit-learn and TensorFlow


Machine Learning with Python, scikit-learn and TensorFlow

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