Machine Learning for Aspiring Data Scientists: Zero to Hero
Machine Learning for Aspiring Data Scientists: Zero to Hero, available at $84.99, has an average rating of 4.5, with 218 lectures, 17 quizzes, based on 62 reviews, and has 1289 subscribers.
You will learn about Undertand the foundations of machine learning even if you're a total beginner Be able to pass the typical machine learning interviews for data science jobs Avoid rookie mistakes that waste companies' time and money Learn machine learning without spending time on mathematical proofs and outdated methods that don't come up in interviews or work. Build machine learning models with Python and Scikit-Learn Understand linear regression, neural networks, random forest, gradient boosting, support vector machines This course is ideal for individuals who are Aspiring data scientists who want to get their first job in the field. or Software engineers who want to be involved in data science and machine learning. or Researchers who want to make the move from academia to industry. or Computer science graduates who want to dabble in data science. It is particularly useful for Aspiring data scientists who want to get their first job in the field. or Software engineers who want to be involved in data science and machine learning. or Researchers who want to make the move from academia to industry. or Computer science graduates who want to dabble in data science.
Enroll now: Machine Learning for Aspiring Data Scientists: Zero to Hero
Summary
Title: Machine Learning for Aspiring Data Scientists: Zero to Hero
Price: $84.99
Average Rating: 4.5
Number of Lectures: 218
Number of Quizzes: 17
Number of Published Lectures: 218
Number of Published Quizzes: 17
Number of Curriculum Items: 235
Number of Published Curriculum Objects: 235
Original Price: $24.99
Quality Status: approved
Status: Live
What You Will Learn
- Undertand the foundations of machine learning even if you're a total beginner
- Be able to pass the typical machine learning interviews for data science jobs
- Avoid rookie mistakes that waste companies' time and money
- Learn machine learning without spending time on mathematical proofs and outdated methods that don't come up in interviews or work.
- Build machine learning models with Python and Scikit-Learn
- Understand linear regression, neural networks, random forest, gradient boosting, support vector machines
Who Should Attend
- Aspiring data scientists who want to get their first job in the field.
- Software engineers who want to be involved in data science and machine learning.
- Researchers who want to make the move from academia to industry.
- Computer science graduates who want to dabble in data science.
Target Audiences
- Aspiring data scientists who want to get their first job in the field.
- Software engineers who want to be involved in data science and machine learning.
- Researchers who want to make the move from academia to industry.
- Computer science graduates who want to dabble in data science.
This course will teach you the foundations of machine learning. The content was especially designed to help you pass machine learning interviews for data science jobs.
The course will help you:
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Pass job interviews and technical quizzes
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Avoid rookie mistakes that waste companies’ time and money
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Be prepared for real work.
Important stuff about this course:
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You won’t spend hours learning stuff that never comes up in a job interview.
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Total beginners are welcome; coding experience or advanced math knowledge are not required.
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It was designed by an industry expert who’s been on the hiring side of the table and knows what companies are looking for.
This course will be of great help if you are:
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A student who wants to prepare for work in data science after graduating.
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An established professional or academic who wants to switch careers to data science.
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A total beginner who wants to dabble in machine learning and data science for the first time.
How is this different from an academic course or a bootcamp?
In academic courses, your teacher spends hours speaking about calculus and linear algebra, but then none of that comes up in a job interview! That in-depth knowledge certainly has a place but is not what most companies are looking for.
In bootcamps you tend to learn how to use many tools but not how they work under the hood. This black-box knowledge is what companies want to avoid the most in applicants!
This course sits in between—you gain foundational knowledgeand truly understand machine learning, without spending time on unimportant stuff.
Course Curriculum
Chapter 1: Machine Learning Models
Lecture 1: Modeling an epidemic
Lecture 2: The machine learning recipe
Lecture 3: The components of a machine learning model
Lecture 4: Why model?
Lecture 5: On assumptions and can we get rid of them?
Lecture 6: The case of AlphaZero
Lecture 7: Overfitting/underfitting/bias/variance
Lecture 8: Why use machine learning
Lecture 9: Notes on machine learning models
Chapter 2: Linear regression
Lecture 1: The InsureMe challenge
Lecture 2: Supervised learning
Lecture 3: A quick note on the word "features"
Lecture 4: Linear assumption
Lecture 5: Linear regression template
Lecture 6: Non-linear vs proportional vs linear
Lecture 7: Linear regression template revisited
Lecture 8: Loss function
Lecture 9: Training algorithm
Lecture 10: Code time
Lecture 11: R squared
Lecture 12: Why use a linear model?
Lecture 13: Kaggle notebook on linear regression
Lecture 14: Notes on supervised learning and linear regression
Lecture 15: Finding closed-form solution to linear regression (optional)
Chapter 3: Scaling and Pipelines
Lecture 1: Introduction to scaling
Lecture 2: Min-max scaling
Lecture 3: Code time (min-max scaling)
Lecture 4: The problem with min-max scaling
Lecture 5: What's your IQ?
Lecture 6: Standard scaling
Lecture 7: Code time (standard scaling)
Lecture 8: Model before and after scaling
Lecture 9: Inference time
Lecture 10: Pipelines
Lecture 11: Code time (pipelines)
Lecture 12: Kaggle notebook on scaling and pipelines
Lecture 13: Notes on scaling and pipelines
Chapter 4: Regularization
Lecture 1: Spurious correlations
Lecture 2: L2 regularization
Lecture 3: Code time (L2 regularization)
Lecture 4: L2 results
Lecture 5: L1 regularization
Lecture 6: Code time (L1 regularization)
Lecture 7: L1 results
Lecture 8: Why does L1 encourage zeros?
Lecture 9: L1 vs L2: Which one is best?
Lecture 10: Kaggle notebook on regularization
Lecture 11: Notes on regularization
Chapter 5: Validation
Lecture 1: Introduction to validation
Lecture 2: Why not evaluate model on training data
Lecture 3: The validation set
Lecture 4: Code time (validation set)
Lecture 5: Error curves
Lecture 6: Model selection
Lecture 7: The problem with model selection
Lecture 8: Tainted validation set
Lecture 9: Monkeys with typewriters
Lecture 10: My own validation epic fail
Lecture 11: The test set
Lecture 12: What if the model doesn't pass the test?
Lecture 13: How not to be fooled by randomness
Lecture 14: Cross-validation
Lecture 15: Code time (cross validation)
Lecture 16: Cross-validation results summary
Lecture 17: AutoML
Lecture 18: Is AutoML a good idea?
Lecture 19: Red flags: Don't do this!
Lecture 20: Red flags summary and what to do instead
Lecture 21: Your job as a data scientist
Lecture 22: Kaggle notebook on validation and cross-validation
Lecture 23: 30-minute code assignment with new dataset!
Lecture 24: Notes on validation and testing
Lecture 25: Extra reading: Model retraining
Lecture 26: Extra reading: The Difference between Statistics and Machine Learning
Chapter 6: Common Mistakes
Lecture 1: Intro and recap
Lecture 2: Mistake #1: Data leakage
Lecture 3: The golden rule
Lecture 4: Helpful trick (feature importance)
Lecture 5: Real example of data leakage (part 1)
Lecture 6: Real example of data leakage (part 2)
Lecture 7: Another (funny) example of data leakage
Lecture 8: Mistake #2: Random split of dependent data
Lecture 9: Another example (insurance data)
Lecture 10: Mistake #3: Look-Ahead Bias
Lecture 11: Example solutions to Look-Ahead Bias
Lecture 12: Consequences of Look-Ahead Bias
Instructors
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Emmanuel Maggiori
Computer Scientist
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 5 votes
- 4 stars: 19 votes
- 5 stars: 38 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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