Course Content
Subtitle Guide – Hướng dẫn thêm phụ đề
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01 Course Introduction
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001 Introduction to the Course
03:33
002 Course Help and Welcome
00:36
02 Environment Set-Up
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004 Python Environment Setup
11:14
03 Jupyter Overview
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006 Jupyter Notebooks
13:48
007 Optional Virtual Environments
09:51
04 Python Crash Course
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008 Welcome to the Python Crash Course Section
00:17
009 Introduction to Python Crash Course
01:26
010 Python Crash Course – Part 1
19:30
011 Python Crash Course – Part 2
15:14
012 Python Crash Course – Part 3
16:39
013 Python Crash Course – Part 4
15:37
014 Python Crash Course Exercises – Overview
03:35
015 Python Crash Course Exercises – Solutions
11:56
05 Python for Data Analysis – NumPy
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016 Welcome to the NumPy Section
00:11
017 Introduction to Numpy
02:13
018 Numpy Arrays
16:50
020 Numpy Array Indexing
18:23
021 Numpy Operations
07:04
022 Numpy Exercises Overview
02:46
023 Numpy Exercises Solutions
15:32
06 Python for Data Analysis – Pandas
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024 Welcome to the Pandas Section
00:14
025 Introduction to Pandas
01:44
026 Series
10:39
027 DataFrames – Part 1
15:31
028 DataFrames – Part 2
17:10
029 DataFrames – Part 3
09:12
030 Missing Data
06:19
031 Groupby
06:49
032 Merging Joining and Concatenating
08:56
033 Operations
12:04
034 Data Input and Output
14:00
07 Python for Data Analysis – Pandas Exercises
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036 SF Salaries Exercise Overview
01:55
037 SF Salaries Solutions
15:26
038 Ecommerce Purchases Exercise Overview
02:11
039 Ecommerce Purchases Exercise Solutions
15:13
08 Python for Data Visualization – Matplotlib
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040 Welcome to the Data Visualization Section
00:22
041 Introduction to Matplotlib
03:02
042 Matplotlib Part 1
16:58
043 Matplotlib Part 2
15:51
044 Matplotlib Part 3
11:52
045 Matplotlib Exercises Overview
01:47
046 Matplotlib Exercises – Solutions
10:19
09 Python for Data Visualization – Seaborn
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047 Introduction to Seaborn
02:58
048 Distribution Plots
18:21
049 Categorical Plots
17:18
050 Matrix Plots
10:14
051 Grids
08:30
052 Regression Plots
07:14
053 Style and Color
08:21
054 Seaborn Exercise Overview
01:53
055 Seaborn Exercise Solutions
07:08
10 Python for Data Visualization – Pandas Built-in Data Visualization
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056 Pandas Built-in Data Visualization
13:27
057 Pandas Data Visualization Exercise
01:23
058 Pandas Data Visualization Exercise- Solutions
08:55
11 Python for Data Visualization – Plotly and Cufflinks
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059 Introduction to Plotly and Cufflinks
03:22
060 Plotly and Cufflinks
18:38
12 Python for Data Visualization – Geographical Plotting
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061 Introduction to Geographical Plotting
00:58
062 Choropleth Maps – Part 1 – USA
19:26
063 Choropleth Maps – Part 2 – World
06:53
064 Choropleth Exercises
03:12
065 Choropleth Exercises – Solutions
10:01
13 Data Capstone Project
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066 Welcome to the Data Capstone Projects
00:17
067 911 Calls Project Overview
02:07
068 911 Calls Solutions – Part 1
14:29
069 911 Calls Solutions – Part 2
17:37
071 Finance Data Project Overview
03:06
072 Finance Project – Solutions Part 1
16:13
073 Finance Project – Solutions Part 2
18:11
074 Finance Project – Solutions Part 3
06:24
14 Introduction to Machine Learning
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076 Welcome to the Machine Learning Section
00:31
077 Supervised Learning Overview
08:21
078 Evaluating Performance – Classification Error Metrics
16:37
079 Evaluating Performance – Regression Error Metrics
05:36
080 Machine Learning with Python
09:27
15 Linear Regression
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081 Linear Regression Theory
04:33
083 Linear Regression with Python – Part 1
18:16
084 Linear Regression with Python – Part 2
07:05
085 Linear Regression Project Overview
02:31
086 Linear Regression Project Solution
18:43
16 Cross Validation and Bias-Variance Trade-Off
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087 Bias Variance Trade-Off
06:25
17 Logistic Regression
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088 Logistic Regression Theory
11:53
089 Logistic Regression with Python – Part 1
17:43
090 Logistic Regression with Python – Part 2
16:57
091 Logistic Regression with Python – Part 3
08:15
092 Logistic Regression Project Overview
01:36
093 Logistic Regression Project Solutions
11:05
18 K Nearest Neighbors
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094 KNN Theory
05:39
095 KNN with Python
19:39
096 KNN Project Overview
01:12
097 KNN Project Solutions
14:14
19 Decision Trees and Random Forests
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098 Introduction to Tree Methods
06:53
099 Decision Trees and Random Forest with Python
13:57
100 Decision Trees and Random Forest Project Overview
03:10
101 Decision Trees and Random Forest Solutions Part 1
12:14
102 Decision Trees and Random Forest Solutions Part 2
08:46
20 Support Vector Machines
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103 SVM Theory
04:36
104 Support Vector Machines with Python
17:52
105 SVM Project Overview
02:21
106 SVM Project Solutions
10:09
21 K Means Clustering
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107 K Means Algorithm Theory
05:15
108 K Means with Python
12:35
109 K Means Project Overview
02:53
110 K Means Project Solutions
16:38
22 Principal Component Analysis
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111 Principal Component Analysis
03:26
112 PCA with Python
16:59
23 Recommender Systems
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113 Recommender Systems
04:14
114 Recommender Systems with Python – Part 1
13:37
115 Recommender Systems with Python – Part 2
13:21
24 Natural Language Processing
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116 Natural Language Processing Theory
05:07
117 NLP with Python – Part 1
16:02
118 NLP with Python – Part 2
18:47
119 NLP with Python – Part 3
17:30
120 NLP Project Overview
02:04
121 NLP Project Solutions
19:26
25 Neural Nets and Deep Learning
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123 Welcome to the Deep Learning Section
00:21
124 Introduction to Artificial Neural Networks (ANN)
02:15
126 Perceptron Model
10:39
127 Neural Networks
07:19
128 Activation Functions
10:39
129 Multi-Class Classification Considerations
10:34
130 Cost Functions and Gradient Descent
18:13
131 Backpropagation
14:47
132 TensorFlow vs Keras
02:13
133 TF Syntax Basics – Part One – Preparing the Data
10:49
134 TF Syntax Basics – Part Two – Creating and Training the Model
13:59
135 TF Syntax Basics – Part Three – Model Evaluation
12:56
136 TF Regression Code Along – Exploratory Data Analysis_part1
09:25
136 TF Regression Code Along – Exploratory Data Analysis_part2
09:25
137 TF Regression Code Along – Exploratory Data Analysis – Continued
13:15
138 TF Regression Code Along – Data Preprocessing and Creating a Model
08:42
139 TF Regression Code Along – Model Evaluation and Predictions
11:23
140 TF Classification Code Along – EDA and Preprocessing
08:05
141 TF Classification – Dealing with Overfitting and Evaluation_part1
08:25
141 TF Classification – Dealing with Overfitting and Evaluation_part2
08:25
142 TensorFlow 2.0 Project Options Overview
01:40
143 TensorFlow 2.0 Project Notebook Overview
07:41
144 Keras Project Solutions – Dealing with Missing Data_part1
10:17
144 Keras Project Solutions – Dealing with Missing Data_part2
10:17
145 Keras Project Solutions – Dealing with Missing Data – Part Two
14:46
146 Keras Project Solutions – Categorical Data
12:02
147 Keras Project Solutions – Data PreProcessing_part1
08:41
147 Keras Project Solutions – Data PreProcessing_part2
08:41
148 Keras Project Solutions – Data PreProcessing
03:45
149 Keras Project Solutions – Creating and Training a Model
03:57
150 Keras Project Solutions – Model Evaluation
09:42
151 Tensorboard_part1
09:11
151 Tensorboard_part2
09:11
26 Big Data and Spark with Python
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152 Welcome to the Big Data Section
00:23
153 Big Data Overview
05:31
154 Spark Overview
09:00
156 AWS Account Set-Up
04:13
158 EC2 Instance Set-Up
16:18
159 SSH with Mac or Linux
04:49
160 PySpark Setup
23:48
161 Lambda Expressions Review
05:26
162 Introduction to Spark and Python
08:17
163 RDD Transformations and Actions
23:09
27 BONUS SECTION THANK YOU
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Python for Data Science and Machine Learning Bootcamp
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