Machine Learning with Python from Scratch
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       Video Length : 10h20m0s
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       Tasks Number : 67
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       Students Enrolled : 1231
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       Medium Level
  • Curriculum
  • 1. Environment Setup
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      L1-Anaconda
      10m0s
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      L2-Jupyter Notebook
      10m0s
  • 2. Data Analysis
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      L1-Introduction
      10m0s
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      L2-Numpy: Array Concept and Math Operations
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      L3-Numpy: Indexing, Slicing and Iterating
      10m0s
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      L4-Numpy: Shape Manipulation
      10m0s
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      L5-Numpy: Linear Algebra
      10m0s
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      L6-Pandas: Data structures and properties
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      L7-Pandas: Operations
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      L8-Pandas: Applying Functions
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      L9-Pandas: Importing and Exporting data
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      L10-Pandas: Merge-Join-Concat-Group by
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      L11-Pandas: Statistics with Pandas
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      L12-Time Series with Pandas
      10m0s
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      L13-Matplotlib basics
      10m0s
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      L14-Matplotlib Subplots and Axes
      10m0s
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      L15-Matplotlib: Object Oriented Method
      10m0s
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      L16-Matplotlib: Color Maps
      10m0s
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      L17-Matplotlib: Statistical Graphs part1
      10m0s
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      L18-Matplotlib: Statistical Graphs part2
      10m0s
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      L19-Seaborn: Basics
      10m0s
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      L20-Seaborn: Color Palette
      10m0s
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      L21-Seaborn: Categorical Data
      10m0s
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      L22-Seaborn: Numerical Data
      10m0s
  • 3. Machine Learning
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      L1-Introduction to Machine Learning
      10m0s
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      L2-Overfitting and Underfitting
      10m0s
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      L3-KFold Cross Validation
      10m0s
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      L4-Classification Metrics
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      L5-Logistic Regression
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      L6-Plotting Decision Boundaries
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      L7-Naive Bayes Classifier
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      L8-Suppor Vector Machines for Classification
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      L9-Decision Trees
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      L10-Random Forest
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      L11-KNN
      10m0s
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      L12-GridSearchCV
      10m0s
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      L13-K-Means
      10m0s
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      L14-Principal Component Analysis(PCA)
      10m0s
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      L15-Linear Discriminant Analysis(LDA)
      10m0s
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      L16-Kernel Principal Component Analysis(KPCA)
      10m0s
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      L17-Ensemble Methods(Bagging)
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      L18-AdaBoost
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      L19-Regression Model and Metrics
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      L20-Linear Regression
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      L21-Regularization with Lasso, Ridge and ElasticNet
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      L22-Polynomial Regression
      10m0s
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      L23-SVM, KNN and Random Forest for Regression
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      L24-RANSAC Regression
      10m0s
  • 4. Neural Networks
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      L1-Neural Networks Concepts-Part 1
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      L2-Neural Networks Concepts-Part 2
      10m0s
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      L3-Loss Functions
      10m0s
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      L4-Activation Functions
      10m0s
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      L5-Optimization of ANNs
      10m0s
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      L6-Constructing an ANN with Python-part1
      10m0s
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      L7-Constructing an ANN with Python-part2
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      L8-Constructing an ANN with Python-part3
      10m0s
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      L9-Perceptron with Scikit Learn
      10m0s
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      L10-Multilayer Perceptron with Scikit Learn
      10m0s
  • 5. Applications
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      L1-Datasets
      10m0s
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      L2-ANN for Regression Part 1
      10m0s
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      L3-ANN for Regression Part 2
      10m0s
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      L4-Recognizing Handwritten Digits
      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 from Scratch


Machine Learning with Python from Scratch

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