SGLearn@Python for Data Science & Machine Learning Bootcamp
SGLearn@Python for Data Science & Machine Learning Bootcamp, available at $199.99, has an average rating of 4.43, with 144 lectures, based on 7 reviews, and has 22 subscribers.
You will learn about Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms This course is ideal for individuals who are This course is meant for people with at least some programming experience It is particularly useful for This course is meant for people with at least some programming experience.
Enroll now: SGLearn@Python for Data Science & Machine Learning Bootcamp
Summary
Title: SGLearn@Python for Data Science & Machine Learning Bootcamp
Price: $199.99
Average Rating: 4.43
Number of Lectures: 144
Number of Published Lectures: 144
Number of Curriculum Items: 144
Number of Published Curriculum Objects: 144
Original Price: S$184.98
Quality Status: approved
Status: Live
What You Will Learn
- Use Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
Who Should Attend
- This course is meant for people with at least some programming experience
Target Audiences
- This course is meant for people with at least some programming experience
Welcome to the SGLearn Series targeted at Singapore-based learnerspicking up new skillsets and competencies.
This course is an adaptation of the same course by Jose Marcial Portilla and is specially produced in collaboration with Jose for Singaporean learners. If you are a Singaporean, you are eligible for the CITREP+ funding scheme, terms and conditions apply.
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Note from Jose ….
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 lecturethis 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:
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Programming with Python
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NumPy with Python
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Using pandas Data Frames to solve complex tasks
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Use pandas to handle Excel Files
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Web scraping with python
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Connect Python to SQL
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Use matplotlib and seaborn for data visualizations
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Use plotly for interactive visualizations
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Machine Learning with SciKit Learn, including:
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Linear Regression
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K Nearest Neighbors
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K Means Clustering
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Decision Trees
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Random Forests
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Natural Language Processing
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Neural Nets and Deep Learning
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Support Vector Machines
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and much, much more!
Enroll in the course and become a data scientist today!
Course Curriculum
Chapter 1: Course Introduction
Lecture 1: Introduction to the Course
Lecture 2: Course Help and Welcome
Lecture 3: Course FAQs
Chapter 2: Environment Set-Up
Lecture 1: Environment Set-up and Installation
Chapter 3: Jupyter Overview
Lecture 1: Jupyter Notebooks
Lecture 2: Optional: Virtual Environments
Chapter 4: Python Crash Course
Lecture 1: Welcome to the Python Crash Course Section!
Lecture 2: Introduction to Python Crash Course
Lecture 3: Python Crash Course – Part 1
Lecture 4: Python Crash Course – Part 2
Lecture 5: Python Crash Course – Part 3
Lecture 6: Python Crash Course – Part 4
Lecture 7: Python Crash Course Exercises – Overview
Lecture 8: Python Crash Course Exercises – Solutions
Chapter 5: Python for Data Analysis – NumPy
Lecture 1: Welcome to the NumPy Section!
Lecture 2: Introduction to Numpy
Lecture 3: Numpy Arrays
Lecture 4: Quick Note on Array Indexing
Lecture 5: Numpy Array Indexing
Lecture 6: Numpy Operations
Lecture 7: Numpy Exercises Overview
Lecture 8: Numpy Exercises Solutions
Chapter 6: Python for Data Analysis – Pandas
Lecture 1: Welcome to the Pandas Section!
Lecture 2: Introduction to Pandas
Lecture 3: Series
Lecture 4: DataFrames – Part 1
Lecture 5: DataFrames – Part 2
Lecture 6: DataFrames – Part 3
Lecture 7: Missing Data
Lecture 8: Groupby
Lecture 9: Merging Joining and Concatenating
Lecture 10: Operations
Lecture 11: Data Input and Output
Chapter 7: Python for Data Analysis – Pandas Exercises
Lecture 1: SF Salaries Exercise Overview
Lecture 2: Note on SF Salary Exercise
Lecture 3: SF Salaries Solutions
Lecture 4: Ecommerce Purchases Exercise Overview
Lecture 5: Ecommerce Purchases Exercise Solutions
Chapter 8: Python for Data Visualization – Matplotlib
Lecture 1: Welcome to the Data Visualization Section!
Lecture 2: Introduction to Matplotlib
Lecture 3: Matplotlib Part 1
Lecture 4: Matplotlib Part 2
Lecture 5: Matplotlib Part 3
Lecture 6: Matplotlib Exercises Overview
Lecture 7: Matplotlib Exercises – Solutions
Chapter 9: Python for Data Visualization – Seaborn
Lecture 1: Introduction to Seaborn
Lecture 2: Distribution Plots
Lecture 3: Categorical Plots
Lecture 4: Matrix Plots
Lecture 5: Regression Plots
Lecture 6: Grids
Lecture 7: Style and Color
Lecture 8: Seaborn Exercise Overview
Lecture 9: Seaborn Exercise Solutions
Chapter 10: Python for Data Visualization – Pandas Built-in Data Visualization
Lecture 1: Pandas Built-in Data Visualization
Lecture 2: Pandas Data Visualization Exercise
Lecture 3: Pandas Data Visualization Exercise- Solutions
Chapter 11: Python for Data Visualization – Plotly and Cufflinks
Lecture 1: Introduction to Plotly and Cufflinks
Lecture 2: Plotly and Cufflinks
Chapter 12: Python for Data Visualization – Geographical Plotting
Lecture 1: Introduction to Geographical Plotting
Lecture 2: Choropleth Maps – Part 1 – USA
Lecture 3: Choropleth Maps – Part 2 – World
Lecture 4: Choropleth Exercises
Lecture 5: Choropleth Exercises – Solutions
Chapter 13: Data Capstone Project
Lecture 1: Welcome to the Data Capstone Projects!
Lecture 2: 911 Calls Project Overview
Lecture 3: 911 Calls Solutions – Part 1
Lecture 4: 911 Calls Solutions – Part 2
Lecture 5: Finance Data Project Overview
Lecture 6: Finance Project – Solutions Part 1
Lecture 7: Finance Project – Solutions Part 2
Lecture 8: Finance Project – Solutions Part 3
Chapter 14: Introduction to Machine Learning
Lecture 1: Welcome to the Machine Learning Section!
Lecture 2: Link for ISLR
Lecture 3: Introduction to Machine Learning
Lecture 4: Machine Learning with Python
Chapter 15: Linear Regression
Lecture 1: Linear Regression Theory
Lecture 2: model_selection Updates for SciKit Learn 0.18
Lecture 3: Linear Regression with Python – Part 1
Lecture 4: Linear Regression with Python – Part 2
Lecture 5: Linear Regression Project Overview
Lecture 6: Linear Regression Project Solution
Chapter 16: Cross Validation and Bias-Variance Trade-Off
Lecture 1: Bias Variance Trade-Off
Chapter 17: Logistic Regression
Instructors
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DioPACT SG
SGLearn
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 3 votes
- 5 stars: 3 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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