Data Science & Machine Learning using Python
  • ondemand_video
       Video Length : 17h00m0s
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       Tasks Number : 120
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       Students Enrolled : 396
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       Medium Level
  • Curriculum
  • 1. Course Introduction
    • videocam
      Welcome & Course Overview
      10m0s
    • videocam
      A Humble Request!
      10m0s
    • videocam
      Download_Course_Material
      10m0s
    • videocam
      Set-up the Environment for the Course
      10m0s
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      Important Note:
      10m0s
  • 2. Python Essentials
    • videocam
      Python data types Part 1
      10m0s
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      Python Data Types Part 2
      10m0s
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      Comparisons Operators, if, else, elif statement
      10m0s
    • videocam
      Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)
      10m0s
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      Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)
      10m0s
    • videocam
      Python Essentials Exercises Overview
      10m0s
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      Python Essentials Exercises Solutions
      10m0s
  • 3. Python for Data Analysis using NumPy
    • videocam
      What is Numpy? A brief introduction and installation instructions.
      10m0s
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      NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.
      10m0s
    • videocam
      NumPy Essentials - Indexing, slicing, broadcasting & boolean masking
      10m0s
    • videocam
      NumPy Essentials - Arithmetic Operations & Universal Functions
      10m0s
    • videocam
      NumPy Essentials Exercises Overview
      10m0s
    • videocam
      NumPy Essentials Exercises Solutions
      10m0s
  • 4. Python for Data Analysis using Pandas
    • videocam
      What is pandas? A brief introduction and installation instructions.
      10m0s
    • videocam
      Pandas Introduction.
      10m0s
    • videocam
      Pandas Essentials - Pandas Data Structures - Series
      10m0s
    • videocam
      Pandas Essentials - Pandas Data Structures - DataFrame
      10m0s
    • videocam
      Pandas Essentials - Hierarchical Indexing
      10m0s
    • videocam
      Pandas Essentials - Handling Missing Data
      10m0s
    • videocam
      Pandas Essentials - Data Wrangling - Combining, merging, joining
      10m0s
    • videocam
      Pandas Essentials - Groupby
      10m0s
    • videocam
      Pandas Essentials - Useful Methods and Operations
      10m0s
    • videocam
      Pandas Essentials - Project 1 (Overview) Customer Purchases Data
      10m0s
    • videocam
      Pandas Essentials - Project 1 (Solutions) Customer Purchases Data
      10m0s
    • videocam
      Pandas Essentials - Project 2 (Overview) Chicago Payroll Data
      10m0s
    • videocam
      Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data
      10m0s
    • videocam
      Pandas Essentials - Project 2 (Solutions Part 2) Chicago Payroll Data
      10m0s
  • 5. Python for Data Visualization using matplotlib
    • videocam
      Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach
      10m0s
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      Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach
      10m0s
    • videocam
      Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach
      10m0s
    • videocam
      Matplotlib Essentials - Exercises Overview
      10m0s
    • videocam
      Matplotlib Essentials - Exercises Solutions
      10m0s
    • videocam
      Matplotlib Essentials (Optional) - Advance
      10m0s
  • 6. Python for Data Visualization using Seaborn
    • videocam
      Seaborn - Introduction & Installation
      10m0s
    • videocam
      Seaborn - Distribution Plots
      10m0s
    • videocam
      Seaborn - Categorical Plots (Part 1)
      10m0s
    • videocam
      Seaborn - Categorical Plots (Part 2)
      10m0s
    • videocam
      Seaborn - Axis Grids
      10m0s
    • videocam
      Seaborn - Matrix Plots
      10m0s
    • videocam
      Seaborn - Regression Plots
      10m0s
    • videocam
      Seaborn - Controlling Figure Aesthetics
      10m0s
    • videocam
      Seaborn - Exercises Overview
      10m0s
    • videocam
      Seaborn - Exercise Solutions
      10m0s
  • 7. Python for Data Visualization using pandas
    • videocam
      Pandas Built-in Data Visualization
      10m0s
    • videocam
      Pandas Data Visualization Exercises Overview
      10m0s
    • videocam
      Panda Data Visualization Exercises Solutions
      10m0s
  • 8. Python for interactive & geographical plotting using Plotly and Cufflinks
    • videocam
      Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1)
      10m0s
    • videocam
      Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2)
      10m0s
    • videocam
      Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview)
      10m0s
    • videocam
      Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions)
      10m0s
  • 9. Capstone Project - Python for Data Analysis & Visualization
    • videocam
      Project 1 - Oil vs Banks Stock Price during recession (Overview)
      10m0s
    • videocam
      Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1)
      10m0s
    • videocam
      Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2)
      10m0s
    • videocam
      Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3)
      10m0s
    • videocam
      Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview)
      10m0s
  • 10. Python for Machine Learning (ML) - scikit-learn - Linear Regression Model
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      Introduction to ML - What, Why and Types.....
      10m0s
    • videocam
      Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff
      10m0s
    • videocam
      scikit-learn - Linear Regression Model - Hands-on (Part 1)
      10m0s
    • videocam
      scikit-learn - Linear Regression Model Hands-on (Part 2)
      10m0s
    • videocam
      scikit-learn - Linear Regression Model (Insurance Data Project Overview)
      10m0s
    • videocam
      scikit-learn - Linear Regression Model (Insurance Data Project Solutions)
      10m0s
  • 11. Python for Machine Learning - scikit-learn - Logistic Regression Model
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      Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity...etc.
      10m0s
    • videocam
      scikit-learn - Logistic Regression Model - Hands-on (Part 1)
      10m0s
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      scikit-learn - Logistic Regression Model - Hands-on (Part 2)
      10m0s
    • videocam
      scikit-learn - Logistic Regression Model - Hands-on (Part 3)
      10m0s
    • videocam
      scikit-learn - Logistic Regression Model - Hands-on (Project Overview)
      10m0s
    • videocam
      scikit-learn - Logistic Regression Model - Hands-on (Project Solutions)
      10m0s
  • 12. Python for Machine Learning - scikit-learn - K Nearest Neighbors
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      Theory: K Nearest Neighbors, Curse of dimensionality ....
      10m0s
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      scikit-learn - K Nearest Neighbors - Hands-on
      10m0s
    • videocam
      scikt-learn - K Nearest Neighbors (Project Overview)
      10m0s
    • videocam
      scikit-learn - K Nearest Neighbors (Project Solutions)
      10m0s
  • 13. Python for Machine Learning - scikit-learn - Decision Tree and Random Forests
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      Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging....
      10m0s
    • videocam
      scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1)
      10m0s
    • videocam
      scikit-learn - Decision Tree and Random Forests (Project Overview)
      10m0s
    • videocam
      scikit-learn - Decision Tree and Random Forests (Project Solutions)
      10m0s
  • 14. Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)
    • videocam
      Support Vector Machines (SVMs) - (Theory Lecture)
      10m0s
    • videocam
      scikit-learn - Support Vector Machines - Hands-on (SVMs)
      10m0s
    • videocam
      scikit-learn - Support Vector Machines (Project 1 Overview)
      10m0s
    • videocam
      scikit-learn - Support Vector Machines (Project 1 Solutions)
      10m0s
    • videocam
      scikit-learn - Support Vector Machines (Optional Project 2 - Overview)
      10m0s
  • 15. Python for Machine Learning - scikit-learn - K Means Clustering
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      Theory: K Means Clustering, Elbow method .....
      10m0s
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      scikit-learn - K Means Clustering - Hands-on
      10m0s
    • videocam
      scikit-learn - K Means Clustering (Project Overview)
      10m0s
    • videocam
      scikit-learn - K Means Clustering (Project Solutions)
      10m0s
  • 16. Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)
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      Theory: Principal Component Analysis (PCA)
      10m0s
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      scikit-learn - Principal Component Analysis (PCA) - Hands-on
      10m0s
    • videocam
      scikit-learn - Principal Component Analysis (PCA) - (Project Overview)
      10m0s
    • videocam
      scikit-learn - Principal Component Analysis (PCA) - (Project Solutions)
      10m0s
  • 17. Recommender Systems with Python - (Additional Topic)
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      Theory: Recommender Systems their Types and Importance
      10m0s
    • videocam
      Python for Recommender Systems - Hands-on (Part 1)
      10m0s
    • videocam
      Python for Recommender Systems - - Hands-on (Part 2)
      10m0s
  • 18. Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)
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      Natural Language Processing (NLP) - (Theory Lecture)
      10m0s
    • videocam
      NLTK - NLP-Challenges, Data Sources, Data Processing .....
      10m0s
    • videocam
      NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing
      10m0s
    • videocam
      NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW....
      10m0s
    • videocam
      NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes ...
      10m0s
    • videocam
      NLTK - NLP - Pipeline feature to assemble several steps for cross-validation...
      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 & Machine Learning using Python


Data Science & Machine Learning using Python

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