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

Resolve the captcha to access the links!