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 youre 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mistakes that waste companies time and money Learn machine learning without spending time on mathematical proofs and outdated methods that dont come up in interviews or work.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 youre 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Learn machine learning without spending time on mathematical proofs and outdated methods that dont come up in interviews or work.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:

Pass job interviews and technical quizzes

Avoid rookie mistakes that waste companies’ time and money

Be prepared for real work.

Important stuff about this course:

You won’t spend hours learning stuff that never comes up in a job interview.

Total beginners are welcome; coding experience or advanced math knowledge are not required.

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:

A student who wants to prepare for work in data science after graduating.

An established professional or academic who wants to switch careers to data science.

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: Whats 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 doesnt 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: Dont 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

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!