| 1. Exploratory Data Analysis.mp4 | 3.3 MB | ||
| 1. Exploratory Data Analysis.srt | 1.7 KB | ||
| 1. Installing Anaconda.mp4 | 23.5 MB | ||
| 1. Installing Anaconda.srt | 5.1 KB | ||
| 1. Model Development.mp4 | 6.3 MB | ||
| 1. Model Development.srt | 2.3 KB | ||
| 1. Model Evaluation and Refinement.mp4 | 25.9 MB | ||
| 1. Model Evaluation and Refinement.srt | 10.7 KB | ||
| 1. Pre-processing Data in Python.mp4 | 7.4 MB | ||
| 1. Pre-processing Data in Python.srt | 3.1 KB | ||
| 1. The Problem.mp4 | 9.3 MB | ||
| 1. The Problem.srt | 2.8 KB | ||
| 2. Dealing with Missing Values in Python.mp4 | 17.7 MB | ||
| 2. Dealing with Missing Values in Python.srt | 8 KB | ||
| 2. Descriptive Statistics.mp4 | 15.7 MB | ||
| 2. Descriptive Statistics.srt | 6.9 KB | ||
| 2. Linear Regression and Multiple Linear Regression.mp4 | 19.7 MB | ||
| 2. Linear Regression and Multiple Linear Regression.srt | 7.9 KB | ||
| 2. Overfitting, Underfitting and Model Selection.mp4 | 14.3 MB | ||
| 2. Overfitting, Underfitting and Model Selection.srt | 6.3 KB | ||
| 2. Understanding the Data.mp4 | 16.7 MB | ||
| 2. Understanding the Data.srt | 3.2 KB | ||
| 2. Your Way Around Jupyter Notebooks.mp4 | 22.3 MB | ||
| 2. Your Way Around Jupyter Notebooks.srt | 7.8 KB | ||
| 3. Data Formatting in Python.mp4 | 12.1 MB | ||
| 3. Data Formatting in Python.srt | 4.3 KB | ||
| 3. Dealing with The Course's Notebooks.mp4 | 5.7 MB | ||
| 3. Dealing with The Course's Notebooks.srt | 1.3 KB | ||
| 3. GroupBy in Python.mp4 | 8.4 MB | ||
| 3. GroupBy in Python.srt | 4.6 KB | ||
| 3. Model Evaluation Using Visualization.mp4 | 15 MB | ||
| 3. Model Evaluation Using Visualization.srt | 6.3 KB | ||
| 3. Python Packages for Data Science.mp4 | 6.7 MB | ||
| 3. Python Packages for Data Science.srt | 3.3 KB | ||
| 3. Ridge Regression.mp4 | 13 MB | ||
| 3. Ridge Regression.srt | 6.2 KB | ||
| 4. Correlation.mp4 | 7.8 MB | ||
| 4. Correlation.srt | 3.5 KB | ||
| 4. Data Normalization in Python.mp4 | 10.9 MB | ||
| 4. Data Normalization in Python.srt | 4.8 KB | ||
| 4. Importing and Exporting Data in Python.mp4 | 17.6 MB | ||
| 4. Importing and Exporting Data in Python.srt | 5.4 KB | ||
| 4. Lab 5 Model Evaluation and Refinement.html | 204.8 B | ||
| 4. Polynomial Regression and Pipelines.mp4 | 15.5 MB | ||
| 4. Polynomial Regression and Pipelines.srt | 6.2 KB | ||
| 5. Binning in Python.mp4 | 7.2 MB | ||
| 5. Binning in Python.srt | 2.6 KB | ||
| 5. Correlation Statistics.mp4 | 11.7 MB | ||
| 5. Correlation Statistics.srt | 4.3 KB | ||
| 5. Getting Started Analyzing Data in Python.mp4 | 17.6 MB | ||
| 5. Getting Started Analyzing Data in Python.srt | 5.4 KB | ||
| 5. Measures for In-Sample Evaluation.mp4 | 13 MB | ||
| 5. Measures for In-Sample Evaluation.srt | 4.8 KB | ||
| 6. Analysis of Variance ANOVA.mp4 | 12.8 MB | ||
| 6. Analysis of Variance ANOVA.srt | 5.1 KB | ||
| 6. Lab 1 Review Introduction.html | 204.8 B | ||
| 6. Prediction and Decision Making.mp4 | 20.2 MB | ||
| 6. Prediction and Decision Making.srt | 7.1 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 | 204.8 B | ||
| 7. Lab 3 Exploratory Data Analysis.html | 204.8 B | ||
| 7. Lab 4 Model Development.html | 204.8 B | ||
| ReadMe.txt | 204.8 B | ||
| Visit Coursedrive.org.url | 102.4 B | ||
| ▲ 67 total files | |||
⚡️⚡️For More Udemy Courses Visit ???????? Course Drive
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
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| 3.1 GB | freecoursewb | 2 weeks | 1 | 33 | |
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| 264.5 MB | freecoursewb | 1 month | 6 | 4 | |
| 1.3 GB | freecoursewb | 4 months | 2 | 0 | |
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Udemy - Intro to Investment Banking, M and A, IPO, Modeling + Free Book Posted by
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