**Python for Data Science and Machine Learning Bootcamp**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 22.5 Hours | 3.67 GB

eLearning | Skill level: All Levels

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

Are you ready to start your path to becoming a Data Scientist!

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We’ll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

Programming with Python

NumPy with Python

Using pandas Data Frames to solve complex tasks

Use pandas to handle Excel Files

Web scraping with python

Connect Python to SQL

Use matplotlib and seaborn for data visualizations

Use plotly for interactive visualizations

Machine Learning with SciKit Learn, including:

Linear Regression

K Nearest Neighbors

K Means Clustering

Decision Trees

Random Forests

Natural Language Processing

Neural Nets and Deep Learning

Support Vector Machines

and much, much more!

**+ Table of Contents**

**Course Introduction**

1 Introduction to the Course

2 Course Help and Welcome

3 Course FAQs

**Environment Set-Up**

4 Environment Set-up and Installation

**Jupyter Overview**

5 Updates to Notebook Zip

6 Jupyter Notebooks

7 Optional Virtual Environments

**Python Crash Course**

8 Welcome to the Python Crash Course Section!

9 Introduction to Python Crash Course

10 Python Crash Course – Part 1

11 Python Crash Course – Part 2

12 Python Crash Course – Part 3

13 Python Crash Course – Part 4

14 Python Crash Course Exercises – Overview

15 Python Crash Course Exercises – Solutions

**Python for Data Analysis – NumPy**

16 Welcome to the NumPy Section!

17 Introduction to Numpy

18 Numpy Arrays

19 Quick Note on Array Indexing

20 Numpy Array Indexing

21 Numpy Operations

22 Numpy Exercises Overview

23 Numpy Exercises Solutions

**Python for Data Analysis – Pandas**

24 Welcome to the Pandas Section!

25 Operations

26 Data Input and Output

27 Introduction to Pandas

28 Series

29 DataFrames – Part 1

30 DataFrames – Part 2

31 DataFrames – Part 3

32 Missing Data

33 Groupby

34 Merging Joining and Concatenating

**Python for Data Analysis – Pandas Exercises**

35 SF Salaries Exercise Overview

36 Note on SF Salary Exercise

37 SF Salaries Solutions

38 Ecommerce Purchases Exercise Overview

39 Ecommerce Purchases Exercise Solutions

**Python for Data Visualization – Matplotlib**

40 Welcome to the Data Visualization Section!

41 Introduction to Matplotlib

42 Matplotlib Part 1

43 Matplotlib Part 2

44 Matplotlib Part 3

45 Matplotlib Exercises Overview

46 Matplotlib Exercises – Solutions

**Python for Data Visualization – Seaborn**

47 Introduction to Seaborn

48 Distribution Plots

49 Categorical Plots

50 Matrix Plots

51 Grids

52 Regression Plots

53 Style and Color

54 Seaborn Exercise Overview

55 Seaborn Exercise Solutions

**Python for Data Visualization – Pandas Built-in Data Visualization**

56 Pandas Built-in Data Visualization

57 Pandas Data Visualization Exercise

58 Pandas Data Visualization Exercise- Solutions

**Python for Data Visualization – Plotly and Cufflinks**

59 Introduction to Plotly and Cufflinks

60 Plotly and Cufflinks

**Python for Data Visualization – Geographical Plotting**

61 Introduction to Geographical Plotting

62 Choropleth Maps – Part 1 – USA

63 Choropleth Maps – Part 2 – World

64 Choropleth Exercises

65 Choropleth Exercises – Solutions

**Data Capstone Project**

66 Welcome to the Data Capstone Projects!

67 Calls Project Overview

68 Calls Solutions – Part 1

69 Calls Solutions – Part 2

70 Bank Data

71 Finance Data Project Overview

72 Finance Project – Solutions Part 1

73 Finance Project – Solutions Part 2

74 Finance Project – Solutions Part 3

**Introduction to Machine Learning**

75 Welcome to the Machine Learning Section!

76 Link for ISLR

77 Introduction to Machine Learning

78 Machine Learning with Python

**Linear Regression**

79 Linear Regression Theory

80 model selection Updates for SciKit Learn 0.18

81 Linear Regression with Python – Part 1

82 Linear Regression with Python – Part 2

83 Linear Regression Project Overview

84 Linear Regression Project Solution

**Cross Validation and Bias-Variance Trade-Off**

85 Bias Variance Trade-Off

**Logistic Regression**

86 Logistic Regression Theory

87 Logistic Regression with Python – Part 1

88 Logistic Regression with Python – Part 2

89 Logistic Regression with Python – Part 3

90 Logistic Regression Project Overview

91 Logistic Regression Project Solutions

**K Nearest Neighbors**

92 KNN Theory

93 KNN with Python

94 KNN Project Overview

95 KNN Project Solutions

**Decision Trees and Random Forests**

96 Introduction to Tree Methods

97 Decision Trees and Random Forest with Python

98 Decision Trees and Random Forest Project Overview

99 Decision Trees and Random Forest Solutions Part 1

100 Decision Trees and Random Forest Solutions Part 2

**Support Vector Machines**

101 SVM Theory

102 Support Vector Machines with Python

103 SVM Project Overview

104 SVM Project Solutions

**K Means Clustering**

105 K Means Algorithm Theory

106 K Means with Python

107 K Means Project Overview

108 K Means Project Solutions

**Principal Component Analysis**

109 Principal Component Analysis

110 PCA with Python

**Recommender Systems**

111 Recommender Systems

112 Recommender Systems with Python – Part 1

113 Recommender Systems with Python – Part 2

**Natural Language Processing**

114 Natural Language Processing Theory

115 NLP with Python – Part 1

116 NLP with Python – Part 2

117 NLP with Python – Part 3

118 NLP Project Overview

119 NLP Project Solutions

**Big Data and Spark with Python**

120 Welcome to the Big Data Section!

121 Lambda Expressions Review

122 Introduction to Spark and Python

123 RDD Transformations and Actions

124 Big Data Overview

125 Spark Overview

126 Local Spark Set-Up

127 AWS Account Set-Up

128 Quick Note on AWS Security

129 EC2 Instance Set-Up

130 SSH with Mac or Linux

131 PySpark Setup

**Neural Nets and Deep Learning**

132 Welcome to the Deep Learning Section!

133 Deep Learning Project – Solutions

134 Neural Network Theory

135 What is TensorFlow

136 Installing Tensorflow 1.10

137 TensorFlow Basics

138 MNIST – Part One

139 MNIST – Part Two

140 Tensorflow Estimators

141 Deep Learning Project

**APPENDIX OLD TENSORFLOW VIDEOS (Version 0.8)**

142 TensorFlow Installation

143 MNIST with Multi-Layer Perceptron – Part 1

144 MNIST with Multi-Layer Perceptron – Part 2

145 MNIST with Multi-Layer Perceptron – Part 3

146 TensorFlow with ContribLearn

147 Tensorflow Project Exercise Overview

148 Tensorflow Project Exercise – Solutions

**BONUS DISCOUNT COUPONS FOR OTHER COURSES**

149 Bonus Lecture Coupons