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Data Science and Machine Learning with Python

Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more!

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Created by Juan E. Galvan
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4.4 (861 ratings)
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30-day money-back guarantee

This course includes

22 h 54 min of video content
Beginner Difficulty
Perpetual Access
Access on mobile and Tablet
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Learning Objectives

Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
Learn data cleaning, processing, wrangling and manipulation
How to create resume and land your first job as a Data Scientist
How to use Python for Data Science
How to write complex Python programs for practical industry scenarios
Learn Plotting in Python (graphs, charts, plots, histograms etc)
Learn to use NumPy for Numerical Data
Machine Learning and its various practical applications
Supervised vs Unsupervised Machine Learning
Learn Regression, Classification, Clustering and Sci-kit learn
Machine Learning Concepts and Algorithms
K-Means Clustering
Use Python to clean, analyze, and visualize data
Building Custom Data Solutions
Statistics for Data Science
Probability and Hypothesis Testing

Table of Contents

140 lectures
22 h 54 min
Who is this course for?
02:43
Data Science + Machine Learning Marketplace
06:55
Data Science Job Opportunities
04:24
Data Science Job Roles
10:23
What is a Data Scientist?
17:00
How To Get a Data Science Job
18:39
Data Science Projects Overview
11:52
Why We Use Python
03:14
What is Data Science?
13:24
What is Machine Learning?
14:22
Machine Learning Concepts and Algorithms
14:42
What is a Deep Learning?
09:44
Machine Learning vs Deep Learning
11:09
What is Programming?
06:03
Why Python for Data Science?
04:35
What is Jupyter?
03:54
What is Google Colab?
03:27
Python Variables, Booleans
11:47
Getting Started with Google Colab
09:07
Python Operators
25:26
Python Numbers and Booleans
07:47
Python Strings
13:12
Python Conditional Statements
13:53
Python For Loops and While Loops
08:07
Python Lists
05:10
More about Lists
15:08
Python Tuples
11:25
Python Dictionaries
20:19
Python Sets
09:41
Compound Data Types and When to use each one?
12:58
Python Functions
14:23
Object-Oriented Programming in Python
18:47
Intro To Statistics
07:11
Descriptive Statistics
06:35
Measure of Variability
12:19
Measure of Variability Continued
09:35
Measures of Variable Relationship
07:37
Inferential Statistics
15:18
Measure of Asymmetry
01:57
Sampling Distribution
07:34
What Exactly is Probability?
03:44
Expected Values
02:38
Relative Frequency
05:15
Hypothesis Testing Overview
09:09
Intro to NumPy Array Data Types
12:58
NumPy Arrays
08:21
NumPy Arrays Basics
11:36
NumPy Array Indexing
09:10
NumPy Array Computations
05:53
Broadcasting
04:32
Introduction to Pandas
15:52
Introduction to Pandas Continued
18:05
Data Visualization Overview
24:49
Different Data Visualization Libraries in Python
12:48
Python Data Visualization Implementation
08:27
Feature Scaling
07:41
Data Cleaning
07:43
Linear Regression Intro
08:17
Gradient Descent
05:59
Linear Regression + Correlation Methods
26:33
Linear Regression Implementation
05:06
Logistic Regression
03:22
KNN Overview
03:01
Parametric vs Non-parametric Models
03:28
EDA on Iris Dataset
22:08
The KNN Intuition
02:16
Implement the KNN algorithm from scratch
11:45
Compare the result with the Sklearn Library
03:47
Hyperparameter tuning using the cross-validation
10:47
The decision boundary visualization
04:55
Manhattan vs Euclidean Distance
11:21
Feature scaling in KNN
06:01
Curse of dimensionality
08:09
KNN use cases
03:32
KNN pros and cons
05:32
Decision Trees Section Overview
04:11
EDA on Adult Dataset
16:53
What is Entropy and Information Gain?
21:50
The Decision Tree ID3 algorithm from scratch Part 1
11:33
The Decision Tree ID3 algorithm from scratch Part 2
07:35
The Decision Tree ID3 algorithm from scratch Part 3
04:07
ID3 - Putting Everything Together
21:23
Evaluating our ID3 implementation
16:51
Compare with Sklearn implementation
08:52
Visualizing the tree
10:15
Plot the features importance
05:51
Decision Trees Hyper-parameters
11:39
Pruning
17:11
[Optional] Gain Ration
02:49
Decision Trees Pros and Cons
07:31
[Project] Predict whether income exceeds $50K/yr - Overview
02:33
Ensemble Learning Section Overview
03:46
What is Ensemble Learning?
13:06
What is Bootstrap Sampling?
08:25
What is Bagging?
05:20
Out-of-Bag Error (OOB Error)
07:47
Implementing Random Forests from scratch Part 1
22:34
Implementing Random Forests from scratch Part 2
06:10
Compare with sklearn implementation
03:41
Random Forests Hyper-Parameters
04:23
Random Forests Pros and Cons
05:25
What is Boosting?
04:41
AdaBoost Part 1
04:10
AdaBoost Part 2
14:33
SVM Outline
05:16
SVM intuition
11:38
Hard vs Soft Margins
13:25
C hyper-parameter
04:17
Kernel Trick
12:18
SVM - Kernel Types
18:13
SVM with Linear Dataset (Iris)
13:35
SVM with Non-linear Dataset
12:50
SVM with Regression
05:51
[Project] Voice Gender Recognition using SVM
04:26
Unsupervised Machine Learning Intro
20:22
Unsupervised Machine Learning Continued
20:48
Data Standardization
19:05
PCA Section Overview
05:12
What is PCA?
09:36
PCA Drawbacks
03:31
PCA Algorithm Steps (Mathematics)
13:12
Covariance Matrix vs SVD
04:58
PCA - Main Applications
02:50
PCA - Image Compression
27:00
PCA Data Preprocessing
14:31
PCA - Biplot and the Screen Plot
17:27
PCA - Feature Scaling and Screen Plot
09:29
PCA - Supervised vs Unsupervised
04:55
PCA - Visualization
07:31
Creating A Data Science Resume
06:45
Data Science Cover Letter
03:33
How to Contact Recruiters
04:20
Getting Started with Freelancing
04:13
Top Freelance Websites
05:35
Personal Branding
04:02
Networking Do's and Don'ts
03:45
Importance of a Website
02:56

Description

In this practical, hands-on course you will learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.

Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.

We will go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib.

NumPy - A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.

Pandas - A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you would find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.

This Machine Learning with Python course dives into the basics of machine learning using Python. You will learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We understand that theory is important to build a solid foundation, we understand that theory alone is not going to get the job done so that is why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!

Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

We are going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills.

The course covers 5 main areas:

  • PYTHON FOR DS+ML COURSE INTRO
  • PYTHON DATA ANALYSIS/VISUALIZATION
  • MATHEMATICS FOR DATA SCIENCE
  • MACHINE LEARNING
  • STARTING A DATA SCIENCE CAREER

By the end of the course you will be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.

About Instructor

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Juan E. Galvan

Digital Entrepreneur | Marketer | Consultant | Visionary

Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skill sets.

Featured Reviews

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Feb 02, 2021

Muhammed

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nice course...
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Mar 02, 2021

Jared

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Nice Course
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Jul 26, 2021

Ahmad

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yes I think up to now, the basic introduction is interesting, thanks for making this course for learning lovers...