NumPy, SciPy, Matplotlib & Pandas A-Z: Machine Learning
NumPy, SciPy, Matplotlib & Pandas A-Z: Machine Learning, available at $19.99, has an average rating of 3.96, with 47 lectures, based on 192 reviews, and has 27574 subscribers.
You will learn about Solid foundation in Python programming, data types, loops, conditionals, functions and more Create and analyze projects via Python NumPy, SciPy, Matplotlib & Pandas Clean data with pandas Series and DataFrames Master data visualization Understanding the NumPy library to efficiently work with arrays, matrices, and perform mathematical operations. Go from absolute beginner to become a confident Python NumPy, Pandas and Matplotlib user This course is ideal for individuals who are All levels of students or Anyone who want to explore the world of Python or This course is for you, if you want a great career It is particularly useful for All levels of students or Anyone who want to explore the world of Python or This course is for you, if you want a great career.
Enroll now: NumPy, SciPy, Matplotlib & Pandas A-Z: Machine Learning
Summary
Title: NumPy, SciPy, Matplotlib & Pandas A-Z: Machine Learning
Price: $19.99
Average Rating: 3.96
Number of Lectures: 47
Number of Published Lectures: 47
Number of Curriculum Items: 47
Number of Published Curriculum Objects: 47
Original Price: $29.99
Quality Status: approved
Status: Live
What You Will Learn
- Solid foundation in Python programming, data types, loops, conditionals, functions and more
- Create and analyze projects via Python NumPy, SciPy, Matplotlib & Pandas
- Clean data with pandas Series and DataFrames
- Master data visualization
- Understanding the NumPy library to efficiently work with arrays, matrices, and perform mathematical operations.
- Go from absolute beginner to become a confident Python NumPy, Pandas and Matplotlib user
Who Should Attend
- All levels of students
- Anyone who want to explore the world of Python
- This course is for you, if you want a great career
Target Audiences
- All levels of students
- Anyone who want to explore the world of Python
- This course is for you, if you want a great career
Are you eager to dive into the core libraries that form the backbone of data manipulation, scientific computing, visualization, and machine learning in Python? Welcome to “NumPy, SciPy, Matplotlib & Pandas A-Z: Machine Learning,” your comprehensive guide to mastering these essential libraries for data science and machine learning.
NumPy, SciPy, Matplotlib, and Pandas are the cornerstone libraries in Python for performing data analysis, scientific computing, and visualizing data. Whether you’re a data enthusiast, aspiring data scientist, or machine learning practitioner, this course will equip you with the skills needed to harness the full potential of these libraries for your data-driven projects.
Key Learning Objectives:
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Learn NumPy’s fundamentals, including arrays, array operations, and broadcasting for efficient numerical computations.
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Explore SciPy’s capabilities for mathematics, statistics, optimization, and more, enhancing your scientific computing skills.
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Master Pandas for data manipulation, data analysis, and transforming datasets to extract valuable insights.
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Dive into Matplotlib to create stunning visualizations, including line plots, scatter plots, histograms, and more to effectively communicate data.
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Understand how these libraries integrate with machine learning algorithms to preprocess, analyze, and visualize data for predictive modeling.
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Apply these libraries to real-world projects, from data cleaning and exploration to building machine learning models.
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Learn techniques to optimize code and make efficient use of these libraries for large datasets and complex computations.
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Gain insights into best practices, tips, and tricks for maximizing your productivity while working with these libraries.
Why Choose This Course?
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This course offers a deep dive into NumPy, SciPy, Matplotlib, and Pandas, ensuring you grasp their core functionalities for data science and machine learning.
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Practice your skills with coding exercises, projects, and practical examples that simulate real-world data analysis scenarios.
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Benefit from the guidance of experienced instructors who are passionate about data science and eager to share their knowledge.
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Enroll once and enjoy lifetime access to the course materials, enabling you to learn at your own pace and revisit concepts whenever necessary.
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Mastery of these libraries is crucial for anyone pursuing a career in data science, machine learning, or scientific computing.
Unlock the power of NumPy, SciPy, Matplotlib, and Pandas for data analysis and machine learning. Enroll today in “NumPy, SciPy, Matplotlib & Pandas A-Z: Machine Learning” and elevate your data science skills. Don’t miss this opportunity to become proficient in these fundamental libraries and enhance your data-driven projects!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction of Python Numpy
Lecture 2: Introduction of Numpy Random
Lecture 3: Introduction of NumPy ufunc
Lecture 4: Introduction of Pyton Pandas
Chapter 2: Python Numpy
Lecture 1: Numpy Creating Arrays
Lecture 2: Numpy Array Indexing
Lecture 3: Numpy Array Slicing
Lecture 4: Numpy Data Types
Lecture 5: Numpy Array Shape
Lecture 6: Numpy Array Reshaping
Chapter 3: NumPy Random
Lecture 1: Numpy Random Data Distribution
Lecture 2: Numpy Random Permutations
Lecture 3: Numpy Seaborn
Lecture 4: Numpy Normal Distribution
Lecture 5: Numpy Binomial Distribution
Lecture 6: Numpy Poisson Distribution
Lecture 7: Numpy Uniform Distribution
Chapter 4: NumPy ufunc
Lecture 1: NumPy ufunc Create
Lecture 2: NumPy ufunc Simple Arithmetic
Lecture 3: NumPy ufunc Rounding Decimals
Lecture 4: NumPy ufunc Logs
Lecture 5: NumPy ufunc Summations
Lecture 6: NumPy ufunc Products
Chapter 5: Python Pandas
Lecture 1: Pandas Series
Lecture 2: Pandas DataFrames
Lecture 3: Pandas Read CSV
Lecture 4: Pandas Read JSON
Lecture 5: Pandas Analyzing DataFrames
Chapter 6: Matplotlib
Lecture 1: Introduction of Matplotlib
Lecture 2: Plotting
Lecture 3: Markers
Lecture 4: Line
Lecture 5: Labels
Lecture 6: Grid
Lecture 7: Subplot
Lecture 8: Scatter
Lecture 9: Bars
Lecture 10: Histograms
Lecture 11: Pie Charts
Chapter 7: SciPy
Lecture 1: Introduction of Python SciPy
Lecture 2: SciPy Constants
Lecture 3: SciPy Optimizers
Lecture 4: SciPy Sparse Data
Lecture 5: SciPy Graphs
Lecture 6: SciPy Spatial Data
Lecture 7: SciPy Matlab Arrays
Lecture 8: SciPy Statistical Significance Tests
Instructors
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Sara Academy
Programmer | Android Developer | Web Designer | Instructor
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 10 votes
- 3 stars: 34 votes
- 4 stars: 80 votes
- 5 stars: 65 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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