Learn Python for Data Science & Machine Learning from A-Z
Learn Python for Data Science & Machine Learning from A-Z, available at $49.99, has an average rating of 4.32, with 140 lectures, based on 1766 reviews, and has 112546 subscribers.
You will learn about 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 it's 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 This course is ideal for individuals who are Students who want to learn about Python for Data Science & Machine Learning It is particularly useful for Students who want to learn about Python for Data Science & Machine Learning.
Enroll now: Learn Python for Data Science & Machine Learning from A-Z
Summary
Title: Learn Python for Data Science & Machine Learning from A-Z
Price: $49.99
Average Rating: 4.32
Number of Lectures: 140
Number of Published Lectures: 140
Number of Curriculum Items: 140
Number of Published Curriculum Objects: 140
Original Price: $99.99
Quality Status: approved
Status: Live
What You Will Learn
- 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 it's 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
Who Should Attend
- Students who want to learn about Python for Data Science & Machine Learning
Target Audiences
- Students who want to learn about Python for Data Science & Machine Learning
Learn Python for Data Science & Machine Learning from A-Z
In this practical, hands-on course you’ll 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’ll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +
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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.
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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’d 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’ll 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 isn’t going to get the job done so that’s 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.
Together we’re 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:
1: PYTHON FOR DS+ML COURSE INTRO
This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.
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Intro to Data Science + Machine Learning with Python
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Data Science Industry and Marketplace
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Data Science Job Opportunities
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How To Get a Data Science Job
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Machine Learning Concepts & Algorithms
2: PYTHON DATA ANALYSIS/VISUALIZATION
This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.
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Python Crash Course
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NumPy Data Analysis
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Pandas Data Analysis
3: MATHEMATICS FOR DATA SCIENCE
This section gives you a full introduction to the mathematics for data science such as statistics and probability.
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Descriptive Statistics
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Measure of Variability
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Inferential Statistics
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Probability
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Hypothesis Testing
4: MACHINE LEARNING
This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.
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Intro to Machine Learning
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Data Preprocessing
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Linear Regression
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Logistic Regression
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K-Nearest Neighbors
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Decision Trees
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Ensemble Learning
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Support Vector Machines
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K-Means Clustering
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PCA
5: STARTING A DATA SCIENCE CAREER
This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.
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Creating a Resume
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Creating a Cover Letter
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Personal Branding
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Freelancing + Freelance websites
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Importance of Having a Website
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Networking
By the end of the course you’ll 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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Who is This Course For?
Lecture 2: Data Science + Machine Learning Marketplace
Lecture 3: Data Science Job Opportunities
Lecture 4: Data Science Job Roles
Lecture 5: What is a Data Scientist?
Lecture 6: How To Get a Data Science Job
Lecture 7: Data Science Projects Overview
Chapter 2: Data Science & Machine Learning Concepts
Lecture 1: Why We Use Python?
Lecture 2: What is Data Science?
Lecture 3: What is Machine Learning?
Lecture 4: Machine Learning Concepts & Algorithms
Lecture 5: What is Deep Learning?
Lecture 6: Machine Learning vs Deep Learning
Chapter 3: Python For Data Science
Lecture 1: What is Programming?
Lecture 2: Why Python for Data Science?
Lecture 3: What is Jupyter?
Lecture 4: What is Google Colab?
Lecture 5: Python Variables, Booleans and None
Lecture 6: Getting Started with Google Colab
Lecture 7: Python Operators
Lecture 8: Python Numbers & Booleans
Lecture 9: Python Strings
Lecture 10: Python Conditional Statements
Lecture 11: Python For Loops and While Loops
Lecture 12: Python Lists
Lecture 13: More about Lists
Lecture 14: Python Tuples
Lecture 15: Python Dictionaries
Lecture 16: Python Sets
Lecture 17: Compound Data Types & When to use each one?
Lecture 18: Python Functions
Lecture 19: Object Oriented Programming in Python
Chapter 4: Statistics for Data Science
Lecture 1: Intro To Statistics
Lecture 2: Descriptive Statistics
Lecture 3: Measure of Variability
Lecture 4: Measure of Variability Continued
Lecture 5: Measures of Variable Relationship
Lecture 6: Inferential Statistics
Lecture 7: Measure of Asymmetry
Lecture 8: Sampling Distribution
Chapter 5: Probability & Hypothesis Testing
Lecture 1: What Exactly is Probability?
Lecture 2: Expected Values
Lecture 3: Relative Frequency
Lecture 4: Hypothesis Testing Overview
Chapter 6: NumPy Data Analysis
Lecture 1: Intro NumPy Array Data Types
Lecture 2: NumPy Arrays
Lecture 3: NumPy Arrays Basics
Lecture 4: NumPy Array Indexing
Lecture 5: NumPy Array Computations
Lecture 6: Broadcasting
Chapter 7: Pandas Data Analysis
Lecture 1: Introduction to Pandas
Lecture 2: Introduction to Pandas Continued
Chapter 8: Python Data Visualization
Lecture 1: Data Visualization Overview
Lecture 2: Different Data Visualization Libraries in Python
Lecture 3: Python Data Visualization Implementation
Chapter 9: Machine Learning
Lecture 1: Introduction To Machine Learning
Chapter 10: Data Loading & Exploration
Lecture 1: Exploratory Data Analysis
Chapter 11: Data Cleaning
Lecture 1: Feature Scaling
Lecture 2: Data Cleaning
Chapter 12: Feature Selecting and Engineering
Lecture 1: Feature Engineering
Chapter 13: Linear and Logistic Regression
Lecture 1: Linear Regression Intro
Lecture 2: Gradient Descent
Lecture 3: Linear Regression + Correlation Methods
Lecture 4: Linear Regression Implementation
Lecture 5: Logistic Regression
Chapter 14: K Nearest Neighbors
Lecture 1: KNN Overview
Lecture 2: parametric vs non-parametric models
Lecture 3: EDA on Iris Dataset
Lecture 4: The KNN Intuition
Lecture 5: Implement the KNN algorithm from scratch
Lecture 6: Compare the result with the sklearn library
Lecture 7: Hyperparameter tuning using the cross-validation
Lecture 8: The decision boundary visualization
Lecture 9: Manhattan vs Euclidean Distance
Lecture 10: Feature scaling in KNN
Lecture 11: Curse of dimensionality
Lecture 12: KNN use cases
Lecture 13: KNN pros and cons
Chapter 15: Decision Trees
Lecture 1: Decision Trees Section Overview
Lecture 2: EDA on Adult Dataset
Lecture 3: What is Entropy and Information Gain?
Lecture 4: The Decision Tree ID3 algorithm from scratch Part 1
Lecture 5: The Decision Tree ID3 algorithm from scratch Part 2
Lecture 6: The Decision Tree ID3 algorithm from scratch Part 3
Lecture 7: ID3 – Putting Everything Together
Instructors
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Juan E. Galvan
Digital Entrepreneur | Business Coach -
Ahmed Wael
Python Instructor | ML Engineer | University TA | Freelancer
Rating Distribution
- 1 stars: 24 votes
- 2 stars: 33 votes
- 3 stars: 215 votes
- 4 stars: 641 votes
- 5 stars: 853 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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