Data Science - Deep Learning and Machine Learning with Python
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       Video Length : 14h40m0s
  • format_list_bulleted
       Tasks Number : 99
  • group
       Students Enrolled : 390
  • equalizer
       Medium Level
  • Curriculum
  • 1. Getting Started
    • videocam
      Introduction
      10m0s
    • videocam
      Getting the Most From This Course
      10m0s
    • videocam
      Getting What You Need
      10m0s
    • videocam
      Installing Enthought Canopy
      10m0s
    • videocam
      Python Basics, Part 1 [Optional]
      10m0s
    • videocam
      Python Basics, Part 2 [Optional]
      10m0s
    • videocam
      Running Python Scripts [Optional]
      10m0s
    • videocam
      Introducing the Pandas Library [Optional]
      10m0s
  • 2. Statistics and Probability Refresher, and Python Practise
    • videocam
      Types of Data
      10m0s
    • videocam
      Mean, Median, Mode
      10m0s
    • videocam
      Using mean, median, and mode in Python
      10m0s
    • videocam
      Variation and Standard Deviation
      10m0s
    • videocam
      Probability Density Function; Probability Mass Function
      10m0s
    • videocam
      Common Data Distributions
      10m0s
    • videocam
      Percentiles and Moments
      10m0s
    • videocam
      A Crash Course in matplotlib
      10m0s
    • videocam
      Covariance and Correlation
      10m0s
    • videocam
      [Exercise] Conditional Probability
      10m0s
    • videocam
      Exercise Solution: Conditional Probability of Purchase by Age
      10m0s
    • videocam
      Bayes' Theorem
      10m0s
  • 3. Predictive Models
    • videocam
      Linear Regression
      10m0s
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      Polynomial Regression
      10m0s
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      Multivariate Regression, and Predicting Car Prices
      10m0s
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      Multi-Level Models
      10m0s
  • 4. Machine Learning with Python
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      Supervised vs. Unsupervised Learning, and Train/Test
      10m0s
    • videocam
      Using Train/Test to Prevent Overfitting a Polynomial Regression
      10m0s
    • videocam
      Bayesian Methods: Concepts
      10m0s
    • videocam
      Implementing a Spam Classifier with Naive Bayes
      10m0s
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      K-Means Clustering
      10m0s
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      Clustering people based on income and age
      10m0s
    • videocam
      Measuring Entropy
      10m0s
    • videocam
      Install GraphViz
      10m0s
    • videocam
      Decision Trees: Concepts
      10m0s
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      Decision Trees: Predicting Hiring Decisions
      10m0s
    • videocam
      Ensemble Learning
      10m0s
    • videocam
      Support Vector Machines (SVM) Overview
      10m0s
    • videocam
      Using SVM to cluster people using scikit-learn
      10m0s
  • 5. Recommender Systems
    • videocam
      User-Based Collaborative Filtering
      10m0s
    • videocam
      Item-Based Collaborative Filtering
      10m0s
    • videocam
      Finding Movie Similarities
      10m0s
    • videocam
      Improving the Results of Movie Similarities
      10m0s
    • videocam
      Making Movie Recommendations to People
      10m0s
    • videocam
      [Exercise] Improve the recommender's results
      10m0s
  • 6. More Data Mining and Machine Learning Techniques
    • videocam
      K-Nearest-Neighbors: Concepts
      10m0s
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      Using KNN to predict a rating for a movie
      10m0s
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      Dimensionality Reduction; Principal Component Analysis
      10m0s
    • videocam
      PCA Example with the Iris data set
      10m0s
    • videocam
      Data Warehousing Overview: ETL and ELT
      10m0s
    • videocam
      Reinforcement Learning
      10m0s
  • 7. Dealing with Real-World Data
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      Bias/Variance Tradeoff
      10m0s
    • videocam
      K-Fold Cross-Validation to avoid overfitting
      10m0s
    • videocam
      Data Cleaning and Normalization
      10m0s
    • videocam
      Cleaning web log data
      10m0s
    • videocam
      Normalizing numerical data
      10m0s
    • videocam
      Detecting outliers
      10m0s
  • 8. Apache Spark: Machine Learning on Big Data
    • videocam
      Warning about Java 10!
      10m0s
    • videocam
      Installing Spark - Part 1
      10m0s
    • videocam
      Installing Spark - Part 2
      10m0s
    • videocam
      Spark Introduction
      10m0s
    • videocam
      Spark and the Resilient Distributed Dataset (RDD)
      10m0s
    • videocam
      Introducing MLLib
      10m0s
    • videocam
      Decision Trees in Spark
      10m0s
    • videocam
      K-Means Clustering in Spark
      10m0s
    • videocam
      TF / IDF
      10m0s
    • videocam
      Searching Wikipedia with Spark
      10m0s
    • videocam
      Using the Spark 2.0 DataFrame API for MLLib
      10m0s
  • 9. Experimental Design
    • videocam
      A/B Testing Concepts
      10m0s
    • videocam
      T-Tests and P-Values
      10m0s
    • videocam
      Hands-on With T-Tests
      10m0s
    • videocam
      Determining How Long to Run an Experiment
      10m0s
    • videocam
      A/B Test Gotchas
      10m0s
  • 10. Deep Learning and Neural Networks
    • videocam
      Deep Learning Pre-Requisites
      10m0s
    • videocam
      The History of Artificial Neural Networks
      10m0s
    • videocam
      Deep Learning in the Tensorflow Playground
      10m0s
    • videocam
      Deep Learning Details
      10m0s
    • videocam
      Introducing Tensorflow
      10m0s
    • videocam
      Using Tensorflow, Part 1
      10m0s
    • videocam
      Using Tensorflow, Part 2
      10m0s
    • videocam
      Introducing Keras
      10m0s
    • videocam
      Using Keras to Predict Political Affiliations
      10m0s
    • videocam
      Convolutional Neural Networks (CNN's)
      10m0s
    • videocam
      Using CNN's for handwriting recognition
      10m0s
    • videocam
      Recurrent Neural Networks (RNN's)
      10m0s
    • videocam
      Using a RNN for sentiment analysis
      10m0s
    • videocam
      The Ethics of Deep Learning
      10m0s
    • videocam
      Learning More about Deep Learning
      10m0s
  • 11. Final Project
    • videocam
      Your final project assignment
      10m0s
    • videocam
      Final project review
      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.

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Data Science - Deep Learning and Machine Learning with Python


Data Science - Deep Learning and Machine Learning with Python

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