Udemy - Intro to Data Science Using Python: Your Best Starting Point [Course Drive]

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size382.5 MB
  • Uploaded Bycoursedrive
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  • Last checkedMay. 14th '20
  • Date uploadedMay. 12th '20
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Intro to Data Science Using Python: Your Best Starting Point

Learn About Data Science And Machine Learning Using Python To Start Your Career In Those Fields. The Best Starting Point






What you'll learn

• Introduction to Data Science
• Data Science Most Used Packages
• Data Wrangling
• Model Development
• Model Refinement
• Model Evaluation Techniques

Requirements

• You must have a previous knowledge of Python
• Other than that, sit tight and watch carefully

Description

Welcome to “Introduction to Data Science Using Python” where you will set a good foot in the fields of Data Science and Machine Learning.
I'm your instructor Ali Desoki and I start from scratch going clearly over all the points in the course along with hands-on practical exercises and projects to summarize all the skills you’ve learned.
This course is designed for Beginners covering all Aspects of what you need to know to start in the fields of data science and machine learning with practice notebooks which summarize all the skills you’ve learned.
At the end of this course, you will be able to analyze and manipulate data with python and be able to start your career in this field.
This course covers a lot of useful and essential topics including:
Introduction to Data Science
Data Science Most Used Packages
Data Wrangling
Model Development
Model Refinement
Model Evaluation Techniques and more...
The ideal student for this course is someone who looks to start in the mentioned fields from scratch.
All you need to know is Python and basic statistics to start this course.
So what are you waiting for! Enroll now and jump-start your career in Data Science and Machine Learning.

Who this course is for:

• Python Developers Who Want To Specialize In the Field Of Data Science or Machine Learning
• Beginners in Data Science and Machine Learning Fields Who Are Looking For A Starting Point to This Career
• Data Science and Machine Learning Learners Who Are Looking For the Basic Knowledge of the Field

Files:

Intro to Data Science Using Python- Your Best Starting Point Intro to Data Science Using Python- Your Best Starting Point 6. Model Evaluation and Refinement
  • 1. Model Evaluation and Refinement.mp4 (25.9 MB)
  • 1. Model Evaluation and Refinement.srt (10.7 KB)
  • 2. Overfitting, Underfitting and Model Selection.mp4 (14.3 MB)
  • 2. Overfitting, Underfitting and Model Selection.srt (6.3 KB)
  • 3. Ridge Regression.mp4 (13.0 MB)
  • 3. Ridge Regression.srt (6.2 KB)
  • 4. Lab 5 Model Evaluation and Refinement.html (0.2 KB)
  • ReadMe.txt (0.2 KB)
  • Visit Coursedrive.org.url (0.1 KB)
    • 1. Pre Introduction Installation and Guides
    • 1. Installing Anaconda.mp4 (23.5 MB)
    • 1. Installing Anaconda.srt (5.1 KB)
    • 2. Your Way Around Jupyter Notebooks.mp4 (22.3 MB)
    • 2. Your Way Around Jupyter Notebooks.srt (7.8 KB)
    • 3. Dealing with The Course's Notebooks.mp4 (5.7 MB)
    • 3. Dealing with The Course's Notebooks.srt (1.3 KB)
    2. Review Introduction
    • 1. The Problem.mp4 (9.3 MB)
    • 1. The Problem.srt (2.8 KB)
    • 2. Understanding the Data.mp4 (16.7 MB)
    • 2. Understanding the Data.srt (3.2 KB)
    • 3. Python Packages for Data Science.mp4 (6.7 MB)
    • 3. Python Packages for Data Science.srt (3.3 KB)
    • 4. Importing and Exporting Data in Python.mp4 (17.6 MB)
    • 4. Importing and Exporting Data in Python.srt (5.4 KB)
    • 5. Getting Started Analyzing Data in Python.mp4 (17.6 MB)
    • 5. Getting Started Analyzing Data in Python.srt (5.4 KB)
    • 6. Lab 1 Review Introduction.html (0.2 KB)
    3. Data Wrangling
    • 1. Pre-processing Data in Python.mp4 (7.4 MB)
    • 1. Pre-processing Data in Python.srt (3.1 KB)
    • 2. Dealing with Missing Values in Python.mp4 (17.7 MB)
    • 2. Dealing with Missing Values in Python.srt (8.0 KB)
    • 3. Data Formatting in Python.mp4 (12.1 MB)
    • 3. Data Formatting in Python.srt (4.3 KB)
    • 4. Data Normalization in Python.mp4 (10.9 MB)
    • 4. Data Normalization in Python.srt (4.8 KB)
    • 5. Binning in Python.mp4 (7.2 MB)
    • 5. Binning in Python.srt (2.6 KB)
    • 6. Turning Categorical Variables into Quantitative Variables in Python.mp4 (5.1 MB)
    • 6. Turning Categorical Variables into Quantitative Variables in Python.srt (2.3 KB)
    • 7. Lab 2 Data Wrangling.html (0.2 KB)
    4. Exploratory Data Analysis
    • 1. Exploratory Data Analysis.mp4 (3.3 MB)
    • 1. Exploratory Data Analysis.srt (1.7 KB)
    • 2. Descriptive Statistics.mp4 (15.7 MB)
    • 2. Descriptive Statistics.srt (6.9 KB)
    • 3. GroupBy in Python.mp4 (8.4 MB)
    • 3. GroupBy in Python.srt (4.6 KB)
    • 4. Correlation.mp4 (7.8 MB)
    • 4. Correlation.srt (3.5 KB)
    • 5. Correlation Statistics.mp4 (11.7 MB)
    • 5. Correlation Statistics.srt (4.3 KB)
    • 6. Analysis of Variance ANOVA.mp4 (12.8 MB)
    • 6. Analysis of Variance ANOVA.srt (5.1 KB)
    • 7. Lab 3 Exploratory Data Analysis.html (0.2 KB)
    5. Model Development
    • 1. Model Development.mp4 (6.3 MB)
    • 1. Model Development.srt (2.3 KB)
    • 2. Linear Regression and Multiple Linear Regression.mp4 (19.7 MB)
    • 2. Linear Regression and Multiple Linear Regression.srt (7.9 KB)
    • 3. Model Evaluation Using Visualization.mp4 (15.0 MB)
    • 3. Model Evaluation Using Visualization.srt (6.3 KB)
    • 4. Polynomial Regression and Pipelines.mp4 (15.5 MB)
    • 4. Polynomial Regression and Pipelines.srt (6.2 KB)
    • 5. Measures for In-Sample Evaluation.mp4 (13.0 MB)
    • 5. Measures for In-Sample Evaluation.srt (4.8 KB)
    • 6. Prediction and Decision Making.mp4 (20.2 MB)
    • 6. Prediction and Decision Making.srt (7.1 KB)
    • 7. Lab 4 Model Development.html (0.2 KB)
    • Visit Coursedrive.org.url (0.1 KB)
    • ReadMe.txt (0.2 KB)

Code:

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