Python for Data Science and Machine Learning Bootcamp

Python for Data Science and Machine Learning Bootcamp
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