Python for Data Science and Machine Learning Bootcamp

24h 49m 42s
English
Paid
November 29, 2024

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!

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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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# Title Duration
1 Introduction to the Course 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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