Python for Data Science and Machine Learning Bootcamp

24h 49m 42s
English
Paid

Course description

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!

Read more about the course

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!

Enroll in the course and become a data scientist today!

Requirements:
  • Some programming experience
  • Admin permissions to download files
Who this course is for:
  • This course is meant for people with at least some programming experience

What you'll learn:

  • Use Python for Data Science and Machine Learning
  • Use Spark for Big Data Analysis
  • Implement Machine Learning Algorithms
  • Learn to use NumPy for Numerical Data
  • Learn to use Pandas for Data Analysis
  • Learn to use Matplotlib for Python Plotting
  • Learn to use Seaborn for statistical plots
  • Use Plotly for interactive dynamic visualizations
  • Use SciKit-Learn for Machine Learning Tasks
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forest and Decision Trees
  • Natural Language Processing and Spam Filters
  • Neural Networks
  • Support Vector Machines

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#1: Introduction to the Course

All Course Lessons (152)

#Lesson TitleDurationAccess
1
Introduction to the Course Demo
03:34
2
Course Help and Welcome
00:37
3
Python Environment Setup
11:15
4
Jupyter Notebooks
13:49
5
Optional: Virtual Environments
09:52
6
Welcome to the Python Crash Course Section!
00:18
7
Introduction to Python Crash Course
01:27
8
Python Crash Course - Part 1
19:31
9
Python Crash Course - Part 2
15:15
10
Python Crash Course - Part 3
16:40
11
Python Crash Course - Part 4
15:38
12
Python Crash Course Exercises - Overview
03:36
13
Python Crash Course Exercises - Solutions
11:57
14
Welcome to the NumPy Section!
00:12
15
Introduction to Numpy
02:14
16
Numpy Arrays
16:51
17
Numpy Array Indexing
18:24
18
Numpy Operations
07:05
19
Numpy Exercises Overview
02:47
20
Numpy Exercises Solutions
15:33
21
Welcome to the Pandas Section!
00:15
22
Introduction to Pandas
01:45
23
Series
10:40
24
DataFrames - Part 1
15:32
25
DataFrames - Part 2
17:11
26
DataFrames - Part 3
09:13
27
Missing Data
06:20
28
Groupby
06:50
29
Merging Joining and Concatenating
08:57
30
Operations
12:05
31
Data Input and Output
14:01
32
SF Salaries Exercise Overview
01:56
33
SF Salaries Solutions
15:27
34
Ecommerce Purchases Exercise Overview
02:12
35
Ecommerce Purchases Exercise Solutions
15:14
36
Welcome to the Data Visualization Section!
00:23
37
Introduction to Matplotlib
03:03
38
Matplotlib Part 1
16:59
39
Matplotlib Part 2
15:52
40
Matplotlib Part 3
11:53
41
Matplotlib Exercises Overview
01:48
42
Matplotlib Exercises - Solutions
10:20
43
Introduction to Seaborn
02:59
44
Distribution Plots
18:22
45
Categorical Plots
17:19
46
Matrix Plots
10:15
47
Grids
08:31
48
Regression Plots
07:15
49
Style and Color
08:22
50
Seaborn Exercise Overview
01:54
51
Seaborn Exercise Solutions
07:09
52
Pandas Built-in Data Visualization
13:28
53
Pandas Data Visualization Exercise
01:24
54
Pandas Data Visualization Exercise- Solutions
08:56
55
Introduction to Plotly and Cufflinks
03:23
56
Plotly and Cufflinks
18:39
57
Introduction to Geographical Plotting
00:59
58
Choropleth Maps - Part 1 - USA
19:27
59
Choropleth Maps - Part 2 - World
06:54
60
Choropleth Exercises
03:13
61
Choropleth Exercises - Solutions
10:02
62
Welcome to the Data Capstone Projects!
00:18
63
911 Calls Project Overview
02:08
64
911 Calls Solutions - Part 1
14:30
65
911 Calls Solutions - Part 2
17:38
66
Finance Data Project Overview
03:07
67
Finance Project - Solutions Part 1
16:14
68
Finance Project - Solutions Part 2
18:12
69
Finance Project - Solutions Part 3
06:25
70
Welcome to the Machine Learning Section!
00:32
71
Supervised Learning Overview
08:22
72
Evaluating Performance - Classification Error Metrics
16:38
73
Evaluating Performance - Regression Error Metrics
05:37
74
Machine Learning with Python
09:28
75
Linear Regression Theory
04:34
76
Linear Regression with Python - Part 1
18:17
77
Linear Regression with Python - Part 2
07:06
78
Linear Regression Project Overview
02:32
79
Linear Regression Project Solution
18:44
80
Bias Variance Trade-Off
06:26
81
Logistic Regression Theory
11:54
82
Logistic Regression with Python - Part 1
17:44
83
Logistic Regression with Python - Part 2
16:58
84
Logistic Regression with Python - Part 3
08:16
85
Logistic Regression Project Overview
01:37
86
Logistic Regression Project Solutions
11:06
87
KNN Theory
05:40
88
KNN with Python
19:40
89
KNN Project Overview
01:13
90
KNN Project Solutions
14:15
91
Introduction to Tree Methods
06:54
92
Decision Trees and Random Forest with Python
13:58
93
Decision Trees and Random Forest Project Overview
03:11
94
Decision Trees and Random Forest Solutions Part 1
12:15
95
Decision Trees and Random Forest Solutions Part 2
08:47
96
SVM Theory
04:37
97
Support Vector Machines with Python
17:53
98
SVM Project Overview
02:22
99
SVM Project Solutions
10:10
100
K Means Algorithm Theory
05:16
101
K Means with Python
12:36
102
K Means Project Overview
02:54
103
K Means Project Solutions
16:39
104
Principal Component Analysis
03:27
105
PCA with Python
17:00
106
Recommender Systems
04:14
107
Recommender Systems with Python - Part 1
13:38
108
Recommender Systems with Python - Part 2
13:22
109
Natural Language Processing Theory
05:08
110
NLP with Python - Part 1
16:03
111
NLP with Python - Part 2
18:48
112
NLP with Python - Part 3
17:31
113
NLP Project Overview
02:05
114
NLP Project Solutions
19:27
115
Welcome to the Deep Learning Section!
00:22
116
Introduction to Artificial Neural Networks (ANN)
02:16
117
Perceptron Model
10:40
118
Neural Networks
07:20
119
Activation Functions
10:40
120
Multi-Class Classification Considerations
10:35
121
Cost Functions and Gradient Descent
18:14
122
Backpropagation
14:48
123
TensorFlow vs Keras
02:14
124
TF Syntax Basics - Part One - Preparing the Data
10:50
125
TF Syntax Basics - Part Two - Creating and Training the Model
14:00
126
TF Syntax Basics - Part Three - Model Evaluation
12:57
127
TF Regression Code Along - Exploratory Data Analysis
18:51
128
TF Regression Code Along - Exploratory Data Analysis - Continued
13:16
129
TF Regression Code Along - Data Preprocessing and Creating a Model
08:43
130
TF Regression Code Along - Model Evaluation and Predictions
11:24
131
TF Classification Code Along - EDA and Preprocessing
08:06
132
TF Classification - Dealing with Overfitting and Evaluation
16:51
133
TensorFlow 2.0 Project Options Overview
01:41
134
TensorFlow 2.0 Project Notebook Overview
07:42
135
Keras Project Solutions - Dealing with Missing Data
20:36
136
Keras Project Solutions - Dealing with Missing Data - Part Two
14:47
137
Keras Project Solutions - Categorical Data
12:03
138
Keras Project Solutions - Data PreProcessing
17:24
139
Keras Project Solutions - Data PreProcessing
03:46
140
Keras Project Solutions - Creating and Training a Model
03:58
141
Keras Project Solutions - Model Evaluation
09:43
142
Tensorboard
18:23
143
Welcome to the Big Data Section!
00:24
144
Big Data Overview
05:32
145
Spark Overview
09:01
146
AWS Account Set-Up
04:14
147
EC2 Instance Set-Up
16:19
148
SSH with Mac or Linux
04:50
149
PySpark Setup
23:49
150
Lambda Expressions Review
05:27
151
Introduction to Spark and Python
08:18
152
RDD Transformations and Actions
23:10

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