Python for Data Science and Machine Learning Bootcamp
About Course
Are you ready to start your path to becoming a Data Scientist!
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!
We’ll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:
- Programming with Python
- NumPy with Python
- Using pandas Data Frames to solve complex tasks
- Use pandas to handle Excel Files
- Web scraping with python
- Connect Python to SQL
- Use matplotlib and seaborn for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with SciKit Learn, including:
- Linear Regression
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Natural Language Processing
- Neural Nets and Deep Learning
- Support Vector Machines
- and much, much more!
Enroll in the course and become a data scientist today!
What Will You Learn?
- Use Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
- Learn to use NumPy for Numerical Data
- Learn to use Pandas for Data Analysis
- Learn to use Matplotlib for Python Plotting
- Learn to use Seaborn for statistical plots
- Use Plotly for interactive dynamic visualizations
- Use SciKit-Learn for Machine Learning Tasks
- K-Means Clustering
- Logistic Regression
- Linear Regression
- Random Forest and Decision Trees
- Natural Language Processing and Spam Filters
- Neural Networks
- Support Vector Machines
Course Content
Subtitle Guide – Hướng dẫn thêm phụ đề
01 Course Introduction
02 Environment Set-Up
03 Jupyter Overview
04 Python Crash Course
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00:17
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01:26
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19:30
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15:14
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16:39
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15:37
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03:35
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11:56
05 Python for Data Analysis – NumPy
06 Python for Data Analysis – Pandas
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00:14
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01:44
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10:39
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15:31
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17:10
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09:12
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06:19
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06:49
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08:56
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12:04
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14:00
07 Python for Data Analysis – Pandas Exercises
08 Python for Data Visualization – Matplotlib
09 Python for Data Visualization – Seaborn
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02:58
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18:21
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17:18
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10:14
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08:30
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07:14
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08:21
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01:53
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07:08
10 Python for Data Visualization – Pandas Built-in Data Visualization
11 Python for Data Visualization – Plotly and Cufflinks
12 Python for Data Visualization – Geographical Plotting
13 Data Capstone Project
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00:17
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02:07
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14:29
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17:37
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03:06
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16:13
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18:11
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06:24
14 Introduction to Machine Learning
15 Linear Regression
16 Cross Validation and Bias-Variance Trade-Off
17 Logistic Regression
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11:53
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17:43
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16:57
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08:15
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01:36
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11:05
18 K Nearest Neighbors
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05:39
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19:39
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01:12
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14:14
19 Decision Trees and Random Forests
20 Support Vector Machines
21 K Means Clustering
22 Principal Component Analysis
23 Recommender Systems
24 Natural Language Processing
25 Neural Nets and Deep Learning
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00:21
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02:15
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10:39
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07:19
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10:39
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10:34
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18:13
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14:47
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02:13
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10:49
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13:59
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12:56
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09:25
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09:25
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13:15
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08:42
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11:23
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08:05
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08:25
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08:25
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01:40
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07:41
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10:17
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10:17
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14:46
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12:02
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08:41
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08:41
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03:45
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03:57
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09:42
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09:11
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09:11
26 Big Data and Spark with Python
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00:23
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05:31
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09:00
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04:13
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16:18
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04:49
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23:48
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05:26
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08:17
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23:09