The #1 Python Data Scientist: Sentiment Analysis & More

The #1 Python Data Scientist: Sentiment Analysis & More

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 16.5 Hours | 6.16 GB


Build Projects with Machine Learning, Text Classification, TensorFlowNumPy, PyPlot, Pandas, and More in Google Colab…

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.

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.

What you’ll 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!


+ Table of Contents

Learn Python for Beginners
1 Learn Python for Beginners Overview
2 Introduction to Python
3 Variables
4 Type Conversion Examples
5 Operators
6 Operators Examples
7 Collections
8 Lists
9 Multidimensional List Examples
10 Tuples Examples
11 Dictionaries Examples
12 Ranges Examples
13 Conditionals
14 If Statements Examples
15 If Statements Variants Examples
16 Loops
17 While Loops Examples
18 For Loops Examples
19 Functions
20 Functions Examples
21 Parameters And Return Values Examples
22 Classes and Objects
23 Classes Examples
24 Objects Examples
25 Inheritance Examples
26 Static Members Examples
27 Summary and Outro
28 Python PDF Resource
29 Source Code ($150 Value)

Learn NumPy for Beginners Course
30 Learn NumPy for Beginners Course Overview
31 Intro to NumPy
32 Installing NumPy
33 Creating NumPy Arrays
34 Creating NumPy Matrices
35 Getting and Setting NumPy Elements
36 Arithmetic Operations on NumPy Arrays
37 NumPy Functions Part 1
38 NumPy Functions Part 2
39 Summary and Outro
40 Source Code ($150 Value)
41 Numpy PDF Resource

Learn Pandas for Beginners Course
42 Learn Pandas for Beginners Course Overview
43 Intro to Pandas
44 Installing Pandas
45 Creating Pandas Series
46 Date Ranges
47 Getting Elements from Series
48 Getting Properties of Series
49 Modifying Series
50 Operations on Series
51 Creating Pandas DataFrames
52 Getting Elements from DataFrames
53 Getting Properties from DataFrames
54 Dataframe Modification
55 DataFrame Operations
56 DataFrame Comparisons and Iteration
57 Reading CSVs
58 Summary and Outro
59 Pandas PDF Resource
60 Source Code ($150 Value)

Learn pyplot for Beginners Course
61 pyplot Course Overview
62 Intro to PyPlot
63 Installing Matplotlib
64 Basic Line Plot
65 Customizing Graphs
66 Plotting Multiple Datasets
67 Bar Chart
68 Pie Chart
69 Histogram
70 D Plotting
71 Course Outro
72 Source Code ($150 Value)

Machine Learning for Beginners Course
73 Machine Learning for Beginners Course Overview
74 Machine Learning Overview
75 Deep Dive into Machine Learning
76 Problems Solved with Machine Learning Part 1
77 Problems Solved with Machine Learning Part 2
78 Types of Machine Learning
79 How Machine Learning Works
80 Common Machine Learning Structures
81 Steps to Build a Machine Learning Program
82 Summary and Outro
83 Machine Learning PDF Resource

Learn TensorFlow for Beginners
84 Learn TensorFlow for Beginners Overview
85 Intro to Tensorflow
86 Installing Tensorflow
87 Intro to Linear Regression
88 Linear Regression Model – Creating Dataset
89 Linear Regression Model – Building the Model
90 Linear Regression Model – Creating a Loss Function
91 Linear Regression Model – Training the Model
92 Linear Regression Model – Testing the Model
93 Summary and Outro
94 TensorFlow PDF Resource
95 Source Code ($150 Value)

Sentiment Analysis Project Classify PositiveNegative Reviews
96 Sentiment Analysis Project Overview
97 How Machines Interpret Text
98 Building the Model Part 1 – Examining Dataset
99 Building the Model Part 2 – Formatting Dataset
100 Building the Model Part 3 – Building the Model
101 Building the Model Part 4 – Training the Model
102 Building the Model Part 5 – Testing the Model
103 Course Summary and Outro
104 Source Code ($150 Value)
105 Sentiment Analysis PDF Resource