English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 165 lectures (24h 54m) | 8.18 GB
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 Python Environment Setup
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 Introduction to Pandas
26 Series
27 DataFrames – Part 1
28 DataFrames – Part 2
29 DataFrames – Part 3
30 Missing Data
31 Groupby
32 Merging Joining and Concatenating
33 Operations
34 Data Input and Output
Python for Data Analysis – Pandas Exercises
35 Note on SF Salary Exercise
36 SF Salaries Exercise Overview
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 READ ME FIRST BEFORE PLOTLY PLEASE!
61 Plotly and Cufflinks
Python for Data Visualization – Geographical Plotting
62 Introduction to Geographical Plotting
63 Choropleth Maps – Part 1 – USA
64 Choropleth Maps – Part 2 – World
65 Choropleth Exercises
66 Choropleth Exercises – Solutions
Data Capstone Project
67 Welcome to the Data Capstone Projects!
68 Calls Project Overview
69 Calls Solutions – Part 1
70 Calls Solutions – Part 2
71 Bank Data
72 Finance Data Project Overview
73 Finance Project – Solutions Part 1
74 Finance Project – Solutions Part 2
75 Finance Project – Solutions Part 3
Introduction to Machine Learning
76 Welcome to Machine Learning. Here are a few resources to get you started!
77 Welcome to the Machine Learning Section!
78 Supervised Learning Overview
79 Evaluating Performance – Classification Error Metrics
80 Evaluating Performance – Regression Error Metrics
81 Machine Learning with Python
Linear Regression
82 Linear Regression Theory
83 model_selection Updates for SciKit Learn 0.18
84 Linear Regression with Python – Part 1
85 Linear Regression with Python – Part 2
86 Linear Regression Project Overview
87 Linear Regression Project Solution
Cross Validation and Bias-Variance Trade-Off
88 Bias Variance Trade-Off
Logistic Regression
89 Logistic Regression Theory
90 Logistic Regression with Python – Part 1
91 Logistic Regression with Python – Part 2
92 Logistic Regression with Python – Part 3
93 Logistic Regression Project Overview
94 Logistic Regression Project Solutions
K Nearest Neighbors
95 KNN Theory
96 KNN with Python
97 KNN Project Overview
98 KNN Project Solutions
Decision Trees and Random Forests
99 Introduction to Tree Methods
100 Decision Trees and Random Forest with Python
101 Decision Trees and Random Forest Project Overview
102 Decision Trees and Random Forest Solutions Part 1
103 Decision Trees and Random Forest Solutions Part 2
Support Vector Machines
104 SVM Theory
105 Support Vector Machines with Python
106 SVM Project Overview
107 SVM Project Solutions
K Means Clustering
108 K Means Algorithm Theory
109 K Means with Python
110 K Means Project Overview
111 K Means Project Solutions
Principal Component Analysis
112 Principal Component Analysis
113 PCA with Python
Recommender Systems
114 Recommender Systems
115 Recommender Systems with Python – Part 1
116 Recommender Systems with Python – Part 2
Natural Language Processing
117 Natural Language Processing Theory
118 NLP with Python – Part 1
119 NLP with Python – Part 2
120 NLP with Python – Part 3
121 NLP Project Overview
122 NLP Project Solutions
Neural Nets and Deep Learning
123 Download TensorFlow Notebooks Here
124 Welcome to the Deep Learning Section!
125 Introduction to Artificial Neural Networks (ANN)
126 Installing Tensorflow
127 Perceptron Model
128 Neural Networks
129 Activation Functions
130 Multi-Class Classification Considerations
131 Cost Functions and Gradient Descent
132 Backpropagation
133 TensorFlow vs Keras
134 TF Syntax Basics – Part One – Preparing the Data
135 TF Syntax Basics – Part Two – Creating and Training the Model
136 TF Syntax Basics – Part Three – Model Evaluation
137 TF Regression Code Along – Exploratory Data Analysis
138 TF Regression Code Along – Exploratory Data Analysis – Continued
139 TF Regression Code Along – Data Preprocessing and Creating a Model
140 TF Regression Code Along – Model Evaluation and Predictions
141 TF Classification Code Along – EDA and Preprocessing
142 TF Classification – Dealing with Overfitting and Evaluation
143 TensorFlow 2.0 Project Options Overview
144 TensorFlow 2.0 Project Notebook Overview
145 Keras Project Solutions – Dealing with Missing Data
146 Keras Project Solutions – Dealing with Missing Data – Part Two
147 Keras Project Solutions – Categorical Data
148 Keras Project Solutions – Data PreProcessing
149 Keras Project Solutions – Data PreProcessing
150 Keras Project Solutions – Creating and Training a Model
151 Keras Project Solutions – Model Evaluation
152 Tensorboard
Big Data and Spark with Python
153 Welcome to the Big Data Section!
154 Big Data Overview
155 Spark Overview
156 Local Spark Set-Up
157 AWS Account Set-Up
158 Quick Note on AWS Security
159 EC2 Instance Set-Up
160 SSH with Mac or Linux
161 PySpark Setup
162 Lambda Expressions Review
163 Introduction to Spark and Python
164 RDD Transformations and Actions
BONUS SECTION THANK YOU!
165 Bonus Lecture
Resolve the captcha to access the links!