Python and TensorFlow Data Science and Iris Speciation

Python and TensorFlow Data Science and Iris Speciation, available at $49.99, has an average rating of 4.5, with 106 lectures, based on 5 reviews, and has 88 subscribers.

You will learn about Graph data with PyPlot Build 3D graphs with PyPlot Customize graphs Use TensorFlow to build a program to categorize irises into different species. Build a classification model Implement logic Track data Implement responsiveness Replace Python lists with NumPy arrays Build data structures Build and use NumPy arrays Use Pandas series Use common array functions Use Pandas Date Ranges Read CSVs with Pandas Use Pandas DataFrames Get elements from a Series Get properties from a series Series operations Modify series Series comparisons and iteration Series operations And much more! This course is ideal for individuals who are Anyone who needs to learn classification or Anyone who needs to learn Python or Anyone who needs to graph with Python or Anyone who needs to know more about machine learning or Anyone who wants to use efficient arrays or Anyone who needs an efficient way to analyze data or Anyone with little to no knowledge of machine learning or Anyone with little to no programming experience or Anyone with no Python experience It is particularly useful for Anyone who needs to learn classification or Anyone who needs to learn Python or Anyone who needs to graph with Python or Anyone who needs to know more about machine learning or Anyone who wants to use efficient arrays or Anyone who needs an efficient way to analyze data or Anyone with little to no knowledge of machine learning or Anyone with little to no programming experience or Anyone with no Python experience.

Enroll now: Python and TensorFlow Data Science and Iris Speciation

Summary

Title: Python and TensorFlow Data Science and Iris Speciation

Price: $49.99

Average Rating: 4.5

Number of Lectures: 106

Number of Published Lectures: 106

Number of Curriculum Items: 106

Number of Published Curriculum Objects: 106

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Graph data with PyPlot

Build 3D graphs with PyPlot

Customize graphs

Use TensorFlow to build a program to categorize irises into different species.

Build a classification model

Implement logic

Track data

Implement responsiveness

Replace Python lists with NumPy arrays

Build data structures

Build and use NumPy arrays

Use Pandas series

Use common array functions

Use Pandas Date Ranges

Read CSVs with Pandas

Use Pandas DataFrames

Get elements from a Series

Get properties from a series

Series operations

Modify series

Series comparisons and iteration

Series operations

And much more!

Who Should Attend

Anyone who needs to learn classification

Anyone who needs to learn Python

Anyone who needs to graph with Python

Anyone who needs to know more about machine learning

Anyone who wants to use efficient arrays

Anyone who needs an efficient way to analyze data

Anyone with little to no knowledge of machine learning

Anyone with little to no programming experience

Anyone with no Python experience

Target Audiences

Anyone who needs to learn classification

Anyone who needs to learn Python

Anyone who needs to graph with Python

Anyone who needs to know more about machine learning

Anyone who wants to use efficient arrays

Anyone who needs an efficient way to analyze data

Anyone with little to no knowledge of machine learning

Anyone with little to no programming experience

Anyone with no Python experience

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.

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“Easy to understand and very clear explanations. So far so good!!!” – Alejandro M.

All source code is included for each project.

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Course Curriculum

Chapter 1: Python Language Basics

Lecture 1: Python Language Basics Introduction

Lecture 2: Intro to Python

Lecture 3: Variables

Lecture 4: Type Conversion Examples

Lecture 5: Operators

Lecture 6: Operators Examples

Lecture 7: Collections

Lecture 8: Lists

Lecture 9: Multidimensional List Examples

Lecture 10: Tuples Examples

Lecture 11: Dictionaries Examples

Lecture 12: Ranges Examples

Lecture 13: Conditionials

Lecture 14: If Statements Examples

Lecture 15: if Statements Variants Examples

Lecture 16: Loops

Lecture 17: While Loops Examples

Lecture 18: For Loops Examples

Lecture 19: Functions

Lecture 20: Functions Examples

Lecture 21: Parameters And Return Values Examples

Lecture 22: Classes and Objects

Lecture 23: Classes Examples

Lecture 24: Objects Examples

Lecture 25: Inheritance Examples

Lecture 26: Static Members Examples

Lecture 27: Summary and Outro

Lecture 28: Intro to Python PDF Resource

Lecture 29: Source Code ($150 Value)

Chapter 2: NumPy Course

Lecture 1: NumPy Course Introduction

Lecture 2: Introduction to NumPy

Lecture 3: Installing NumPy

Lecture 4: Creating NumPy Arrays

Lecture 5: Creating NumPy Matrices

Lecture 6: Getting and Setting NumPy Elements

Lecture 7: Arithmetic Operations on NumPy Arrays

Lecture 8: NumPy Functions Part 1

Lecture 9: NumPy Functions Part 2

Lecture 10: Summary and Outro

Lecture 11: Source Code ($150 Value)

Lecture 12: Numpy PDF Resource

Chapter 3: Pandas Course

Lecture 1: Panda Course Introduction

Lecture 2: Introduction to Pandas

Lecture 3: Installing Pandas

Lecture 4: Creating Pandas Series

Lecture 5: Date Ranges

Lecture 6: Getting Elements from Series

Lecture 7: Getting Properties of Series

Lecture 8: Modifying Series

Lecture 9: Operations on Series

Lecture 10: Creating Pandas DataFrames

Lecture 11: Getting Elements from DataFrames

Lecture 12: Getting Properties from DataFrames

Lecture 13: Dataframe Modification

Lecture 14: DataFrame Operations

Lecture 15: DataFrame Comparisons and Iteration

Lecture 16: Reading CSVs

Lecture 17: Summary and Outro

Lecture 18: Pandas PDF Resource

Lecture 19: Source Code ($150 Value)

Chapter 4: PyPlot Course

Lecture 1: PyPlot Course Introduction

Lecture 2: Introduction to PyPlot

Lecture 3: Installing Matplotlib

Lecture 4: Basic Line Plot

Lecture 5: Customizing Graphs

Lecture 6: Plotting Multiple Datasets

Lecture 7: Bar Chart

Lecture 8: Pie Chart

Lecture 9: Histogram

Lecture 10: 3D Plotting

Lecture 11: Course Outro

Lecture 12: Source Code ($150 Value)

Chapter 5: Machine Learning Course

Lecture 1: Machine Learning Course Introduction

Lecture 2: Introduction to Machine Learning

Lecture 3: Deep Dive into Machine Learning

Lecture 4: Problems Solved with Machine Learning Part 1

Lecture 5: Problems Solved with Machine Learning Part 2

Lecture 6: Types of Machine Learning

Lecture 7: How Machine Learning Works

Lecture 8: Common Machine Learning Structures

Lecture 9: Steps to Build a Machine Learning Program

Lecture 10: Summary and Outro

Lecture 11: Intro to Machine Learning PDF Resource

Chapter 6: TensorFlow Course

Lecture 1: TensorFlow Course Introduction

Lecture 2: Introduction to Tensorflow

Lecture 3: Installing Tensorflow

Lecture 4: Intro to Linear Regression

Lecture 5: Linear Regression Model – Creating Dataset

Lecture 6: Linear Regression Model – Building the Model

Lecture 7: Linear Regression Model – Creating a Loss Function

Lecture 8: Linear Regression Model – Training the Model

Lecture 9: Linear Regression Model – Testing the Model

Lecture 10: Summary and Outro

Lecture 11: Intro to Tensorflow PDF Resource

Instructors

Mammoth Interactive
Top-Rated Instructor, 3.3 Million+ Students

John Bura
Best Selling Instructor Web/App/Game Developer 1Mil Students

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 1 votes

4 stars: 1 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!