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The #1 Python Data Scientist: Sentiment Analysis & More

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The #1 Python Data Scientist: Sentiment Analysis & More, available at $59.99, has an average rating of 4.3, with 105 lectures, based on 27 reviews, and has 188 subscribers.

Rating4.3
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You will learn about Process text data Interpret sentiment in reviews Build a model to predict whether a review is positive or negative Implement logic Track data Customize graphs Implement responsiveness Build data structures Graph data with PyPlot Build 3D graphs with PyPlot Use common array functions Replace Python lists with NumPy arrays Build and use NumPy arrays Use Pandas series 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 sentiment analysis and more or Anyone who needs to learn Python or Anyone who needs to know more about machine learning or Anyone who needs to graph with Python or Anyone with no Python experience or Anyone who needs an efficient way to analyze data or Anyone with little to no programming experience or Anyone who wants to use efficient arrays or Anyone with little to no knowledge of machine learning It is particularly useful for Anyone who needs to learn sentiment analysis and more or Anyone who needs to learn Python or Anyone who needs to know more about machine learning or Anyone who needs to graph with Python or Anyone with no Python experience or Anyone who needs an efficient way to analyze data or Anyone with little to no programming experience or Anyone who wants to use efficient arrays or Anyone with little to no knowledge of machine learning.

Enroll now: The #1 Python Data Scientist: Sentiment Analysis & More

Summary

Title: The #1 Python Data Scientist: Sentiment Analysis & More

Price: $59.99

Average Rating: 4.3

Number of Lectures: 105

Number of Published Lectures: 105

Number of Curriculum Items: 105

Number of Published Curriculum Objects: 105

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Process text data
  • Interpret sentiment in reviews
  • Build a model to predict whether a review is positive or negative
  • Implement logic
  • Track data
  • Customize graphs
  • Implement responsiveness
  • Build data structures
  • Graph data with PyPlot
  • Build 3D graphs with PyPlot
  • Use common array functions
  • Replace Python lists with NumPy arrays
  • Build and use NumPy arrays
  • Use Pandas series
  • 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 sentiment analysis and more
  • Anyone who needs to learn Python
  • Anyone who needs to know more about machine learning
  • Anyone who needs to graph with Python
  • Anyone with no Python experience
  • Anyone who needs an efficient way to analyze data
  • Anyone with little to no programming experience
  • Anyone who wants to use efficient arrays
  • Anyone with little to no knowledge of machine learning

Target Audiences

  • Anyone who needs to learn sentiment analysis and more
  • Anyone who needs to learn Python
  • Anyone who needs to know more about machine learning
  • Anyone who needs to graph with Python
  • Anyone with no Python experience
  • Anyone who needs an efficient way to analyze data
  • Anyone with little to no programming experience
  • Anyone who wants to use efficient arrays
  • Anyone with little to no knowledge of machine learning

Learn everything you need to become a data scientist.

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.

This is a once in a lifetime chance to enroll in a massive course.

Absolutely no experience necessary.Start with a complete introduction to Python that is perfect for absolute beginners and can also be used a review.

Jump into using the most popular libraries and frameworks for working with Python. You’ll learn everything you need to become a data scientist. This includes:

0. Python Crash Course for Beginners

Learn Python with project based examples. Get up and running even if you have no programming experience. Superboost your career by masterig the core Python fundamentals.

1. Data Science with NumPy

Build projects with NumPy, the #1 Python library for data science providing arrays and matrices.

2. Data Analysis with Pandas

Build projects with pandas, a software library written for the Python programming language for data manipulation and analysis.

2. Data Visualization with PyPlot

Build projects with pyplot, a MATLAB-like plotting framework enabling you to create a figure, create a plotting area in a figure, plot lines in a plotting area, decorate the plot with labels and much more. Learn it all in this massive course.

3. Machine Learning Theory

Machine learning is in high demand and is quickly becoming a requirement on every software engineer’s resume. Learn how to solve problems with machine learning before diving into practical examples.

4. Introduction to TensorFlow

Build projects with TensorFlow, the most popular platform enabling ML developers to build and deploy machine learning applications such as neural networks. Build your first linear regression model with TensorFlow. Learn how to build a dataset, model, train and test!

5. Build a Sentiment Analysis Model to Classify Reviews as Positive or Negative

All source code is included for each project.

If you buy one course this year, this is it. Sign up while spots are open.

Course Curriculum

Chapter 1: Learn Python for Beginners

Lecture 1: Learn Python for Beginners Overview

Lecture 2: Introduction 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: Conditionals

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: Python PDF Resource

Lecture 29: Source Code ($150 Value)

Chapter 2: Learn NumPy for Beginners Course

Lecture 1: Learn NumPy for Beginners Course Overview

Lecture 2: Intro 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: Learn Pandas for Beginners Course

Lecture 1: Learn Pandas for Beginners Course Overview

Lecture 2: Intro 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: Learn pyplot for Beginners Course

Lecture 1: pyplot Course Overview

Lecture 2: Intro 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 for Beginners Course

Lecture 1: Machine Learning for Beginners Course Overview

Lecture 2: Machine Learning Overview

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: Machine Learning PDF Resource

Chapter 6: Learn TensorFlow for Beginners

Lecture 1: Learn TensorFlow for Beginners Overview

Lecture 2: Intro 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: TensorFlow PDF Resource

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 0 votes
  • 3 stars: 2 votes
  • 4 stars: 12 votes
  • 5 stars: 12 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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