Python and TensorFlow Data Science and Iris Speciation
Use TensorFlow to build a program to categorize irises into different species. And much more!
Master Machine Learning, PyPlot, NumPy, Pandas, Data Science, Iris Speciation with TensorFlow & Land a High Paying Job
Machine learning allows you to build more powerful, more accurate and more user friendly software that can better respond and adapt.
Many companies are integrating machine learning or have already done so, including the biggest Google, Facebook, Netflix, and Amazon.
There are many high paying machine learning jobs.
Jump into this fun and exciting course to land your next interesting and high paying job with the projects you’ll build and problems you’ll learn how to solve.
In just a matter of hours you'll have new skills with projects to back them up:
- Deep dive into machine learning
- Problems that machine learning solves
- Types of machine learning
- Common machine learning structures
- Steps to building a machine learning model
- Build a linear regression machine learning model with TensorFlow
- Test and train the model
- Python variables and operators
- Collection types
- Conditionals and loops
- Functions
- Classes and objects
- Install and import NumPy
- Build NumPy arrays
- Multidimensional NumPy arrays
- Array indexes and properties
- NumPy functions
- NumPy operations
- And much more!
Add new skills to your resume in this project based course:
- Graph data with PyPlot
- Customize graphs
- Build 3D graphs with PyPlot
- Use TensorFlow to build a program to categorize irises into different species.
- Build a classification model
- Track data
- Implement logic
- Implement responsiveness
- Build data structures
- Replace Python lists with NumPy arrays
- Build and use NumPy arrays
- Use common array functions
- Use Pandas series
- Use Pandas Date Ranges
- Use Pandas DataFrames
- Read CSVs with Pandas
- Install and import Pandas
- Build Pandas Series and DataFrames
- Get elements from a Series
- Get properties from a series
- Modify series
- Series operations
- Series comparisons and iteration
- And much more!
Machine learning is quickly becoming a required skill for every software developer.
Enroll now to learn everything you need to know to get up to speed, whether you're a developer or aspiring data scientist. This is the course for you.
Your complete Python course for image recognition, data analysis, data visualization and more.
Reviews On Our Python Courses:
- "I know enough Python to be dangerous. Most of the ML classes are so abstract and theoretical that no learning happens. This is the first class where we use concrete examples that I can relate to and allow me to learn. Absolutely love this course!" - Mary T.
- "Yes, this is an amazing start. For someone new in python this is a very simple boot course. I am able to relate to my earlier programming experience with ease!" - Gajendran C.
- "Clear and concise information" - Paul B.
- "Easy to understand and very clear explanations. So far so good!!!" - Alejandro M.
All source code is included for each project.
Don't miss out! Sign up to join the community.
Your Instructor
Nimish Narang is Mammoth Interactive's lead developer specializing in Python, iOS and Android. Primarily a coder, he also is an avid trader.
Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard’s edX, Business Insider and more.
Over 11 years, Mammoth Interactive has built a global student community with 1.1 million courses sold. Mammoth Interactive has released over 250 courses and 2,500 hours of video content.
Founder and CEO John Bura has been programming since 1997 and teaching
since 2002. John has created top-selling applications for iOS, Xbox and
more. John also runs SaaS company Devonian Apps, building
efficiency-minded software for technology workers like you.
Course Curriculum
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Preview00. Introduction (4:47)
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Preview01. Intro To Python (5:46)
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Preview02. Variables (19:34)
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Start03. Type Conversion Examples (10:21)
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Start04. Operators (7:21)
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Start05. Operators Examples (22:09)
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Start06. Collections (8:39)
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Start07. Lists (11:55)
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Start08. Multidimensional List Examples (8:22)
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Start09. Tuples Examples (8:51)
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Start10. Dictionaries Examples (14:41)
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Start11. Ranges Examples (8:46)
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Start12. Conditionals (6:58)
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Start13. If Statement Examples (10:32)
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Start14. If Statement Variants Examples (11:35)
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Start15. Loops (7:17)
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Start16. While Loops Examples (11:47)
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Start17. For Loops Examples (11:35)
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Start18. Functions (8:04)
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Start19. Functions Examples (9:33)
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Start20. Parameters And Return Values Examples (14:08)
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Start21. Classes and Objects (11:30)
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Start22. Classes Example (13:28)
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Start23. Objects Examples (10:10)
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Start24. Inheritance Examples (17:43)
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Start25. Static Members Example (11:20)
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Start26. Summary and Outro (4:23)
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StartIntro to Python Slides
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StartPython_Language_Basics Code
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Start00. Course Intro (5:11)
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Start01. Intro to Numpy (6:20)
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Start02. Installing Numpy (3:59)
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Start03. Creating Numpy Arrays (16:55)
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Start04. Creating Numpy Matrices (11:57)
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Start05. Getting and Setting Numpy Elements (16:59)
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Start06. Arithmetic Operations on Numpy Arrays (11:56)
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Start07. Numpy Functions Part 1 (19:13)
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Start08. Numpy Functions Part 2 (12:36)
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Start09. Summary and Outro (3:01)
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StartNumpy_Code
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StartNumpy Slides
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Start00. Panda Course Introduction (5:43)
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Start01. Intro to Pandas (7:55)
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Start02. Installing Pandas (5:28)
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Start03. Creating Pandas Series (20:34)
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Start04. Date Ranges (11:29)
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Start05. Getting Elements from Series (19:20)
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Start06. Getting Properties of Series (13:04)
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Start07. Modifying Series (19:01)
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Start08. Operations on Series (11:48)
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Start09. Creating Pandas DataFrames (22:57)
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Start10. Getting Elements from DataFrames (25:12)
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Start11. Getting Properties from DataFrames (17:44)
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Start12. Dataframe Modification (36:24)
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Start13. DataFrame Operations (20:09)
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Start14 DataFrame Comparisons and Iteration (15:35)
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Start15. Reading CSVs (12:00)
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Start16.Summary and Outro (4:14)
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StartpandasPracticeCSV
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StartPandas_Code
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StartPandas Slides