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The Complete Machine Learning Course: From Zero to Expert!

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The Complete Machine Learning Course: From Zero to Expert!, available at $69.99, has an average rating of 3.95, with 103 lectures, based on 49 reviews, and has 1492 subscribers.

You will learn about Master Machine Learning in Python Become an advanced, confident, and modern Machine Learning developer from scratch Become job-ready by understanding how Machine Learning really works behind the scenes Apply robust Data Science techniques for Machine Learning How to think and work like a data scientist: problem-solving, researching, workflows Get fast and friendly support in the Q&A area Machine Learning fundamentals: Supervised, Unsupervised and Reinforcement Learning Master all Machine Learning python libraries: numpy, scipy, pandas, scikit-learn, matplotlib, seaborn, imblearn, notebook… Handle specific topics like Multilayer Perceptron/Neural Networks, Deep Learning and Clustering Be an expert in Support Vector Machines and Kernels Master Decision Trees/Regression and Combination of Classifiers Practice your skills with 50+ challenges and assignments (solutions included) This course is ideal for individuals who are Anyone interested in Machine Learning or Any people who have been trying to learn Machine Learning but: 1) still don't really understand it, or 2) still don't feel confident to take a job interview or Any students in college who want to start a career in Data Science or Anyone interested in working as a Data Scientist or Any data analysts who want to level up in Machine Learning or Any people who want to create added value to their business by using powerful Machine Learning tools. or Anyone who wants to work as a Data Analyst in research, economics, finance, marketing, engineering or medical sectors It is particularly useful for Anyone interested in Machine Learning or Any people who have been trying to learn Machine Learning but: 1) still don't really understand it, or 2) still don't feel confident to take a job interview or Any students in college who want to start a career in Data Science or Anyone interested in working as a Data Scientist or Any data analysts who want to level up in Machine Learning or Any people who want to create added value to their business by using powerful Machine Learning tools. or Anyone who wants to work as a Data Analyst in research, economics, finance, marketing, engineering or medical sectors.

Enroll now: The Complete Machine Learning Course: From Zero to Expert!

Summary

Title: The Complete Machine Learning Course: From Zero to Expert!

Price: $69.99

Average Rating: 3.95

Number of Lectures: 103

Number of Published Lectures: 97

Number of Curriculum Items: 103

Number of Published Curriculum Objects: 97

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Master Machine Learning in Python
  • Become an advanced, confident, and modern Machine Learning developer from scratch
  • Become job-ready by understanding how Machine Learning really works behind the scenes
  • Apply robust Data Science techniques for Machine Learning
  • How to think and work like a data scientist: problem-solving, researching, workflows
  • Get fast and friendly support in the Q&A area
  • Machine Learning fundamentals: Supervised, Unsupervised and Reinforcement Learning
  • Master all Machine Learning python libraries: numpy, scipy, pandas, scikit-learn, matplotlib, seaborn, imblearn, notebook…
  • Handle specific topics like Multilayer Perceptron/Neural Networks, Deep Learning and Clustering
  • Be an expert in Support Vector Machines and Kernels
  • Master Decision Trees/Regression and Combination of Classifiers
  • Practice your skills with 50+ challenges and assignments (solutions included)

Who Should Attend

  • Anyone interested in Machine Learning
  • Any people who have been trying to learn Machine Learning but: 1) still don't really understand it, or 2) still don't feel confident to take a job interview
  • Any students in college who want to start a career in Data Science
  • Anyone interested in working as a Data Scientist
  • Any data analysts who want to level up in Machine Learning
  • Any people who want to create added value to their business by using powerful Machine Learning tools.
  • Anyone who wants to work as a Data Analyst in research, economics, finance, marketing, engineering or medical sectors

Target Audiences

  • Anyone interested in Machine Learning
  • Any people who have been trying to learn Machine Learning but: 1) still don't really understand it, or 2) still don't feel confident to take a job interview
  • Any students in college who want to start a career in Data Science
  • Anyone interested in working as a Data Scientist
  • Any data analysts who want to level up in Machine Learning
  • Any people who want to create added value to their business by using powerful Machine Learning tools.
  • Anyone who wants to work as a Data Analyst in research, economics, finance, marketing, engineering or medical sectors

You’ve just stumbled upon the most complete, in-depth Machine Learning course online.

Whether you want to:

– build the skills you need to get your first data science job

– move to a more senior software developer position

– become a computer scientist mastering in data science

– or just learn Machine Learning to be able to create your own projects quickly.

…this complete Machine Learning Masterclass is the course you need to do all of this, and more.

This course is designed to give you the machine learning skills you need to become a data science expert. By the end of the course, you will understand the machine learning method extremely well and be able to apply it in your own data science projects and be productive as a computer scientist and developer.

What makes this course a bestseller?

Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.

Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Machine Learning course. It’s designed with simplicity and seamless progression in mind through its content.

This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core machine learning skills and master data science. It’s a one-stop shop to learn machine learning. If you want to go beyond the core content you can do so at any time.

What if I have questions?

As if this course wasn’t complete enough, I offer full support, answering any questions you have.

This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.

There’s no risk either!

This course comes with a full 30-day money-back guarantee. Meaning if you are not completely satisfied with the course or your progress, simply let me know and I’ll refund you 100%, every last penny no questions asked.

You either end up with Machine Learning skills, go on to develop great programs and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…

You literally can’t lose.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And as a bonus, this course includes Python code templateswhich you can download and use on your own projects.

Ready to get started, developer?

Enroll nowusing the “Add to Cart” button on the right, and get started on your way to creative, advanced Machine Learning brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, Machine Learning is waiting!)

Course Curriculum

Chapter 1: Code Environment Setup

Lecture 1: Google Colab for Programming in Python

Chapter 2: Machine Learning Fundamentals

Lecture 1: Introduction to Machine Learning

Lecture 2: Supervised Learning

Lecture 3: Unsupervised Learning

Chapter 3: Introduction – Preprocessing and Analysis

Lecture 1: Initial Study of the Dataset

Lecture 2: Basic Visualization

Chapter 4: Visualization – Principal Component Analysis

Lecture 1: Introduction to PCA

Lecture 2: Introduction to the Dataset

Lecture 3: Initial Visualization

Lecture 4: Using PCA

Chapter 5: Visualization – Locally Linear Embedding (LLE)

Lecture 1: Introduction to LLE

Lecture 2: Locally Linear Embedding Algorithm

Lecture 3: Introduction to the Dataset

Lecture 4: Using LLE

Lecture 5: LLE with 3 Dimensions

Chapter 6: Visualization – t-Stochastic Neighbor Embedding (t-SNE)

Lecture 1: Introduction to t-SNE

Lecture 2: Dataset

Lecture 3: Introduction to the Dataset

Lecture 4: t-SNE on Raw Data

Lecture 5: t-SNE on Scaled Data

Lecture 6: t-SNE on Standardized Data

Chapter 7: Visualization – Multidimensional Scaling (MDS)

Lecture 1: Introduction to MDS

Lecture 2: Using MDS with 2 Dimensions

Lecture 3: Using MDS with 3 Dimensions

Chapter 8: Visualization – ISOMAP

Lecture 1: Introducción to ISOMAP

Lecture 2: ISOMAP with 2 Dimensions

Lecture 3: ISOMAP with 3 Dimensions

Chapter 9: Visualization – Fisher Discriminant Analysis

Lecture 1: Introduction to Fisher Discriminant Analysis

Lecture 2: Dataset Information

Lecture 3: Introduction to the Dataset

Lecture 4: Fisher Discriminant Analysis with 2 Dimensions

Lecture 5: Fisher Discriminant Analysis with 3 Dimensions

Chapter 10: Visualization Final Project – Images

Lecture 1: Images

Lecture 2: Introduction to the Image Dataset

Lecture 3: Fisher Discriminant Analysis

Lecture 4: Locally Linear Embedding

Lecture 5: Principal Component Analysis

Lecture 6: ISOMAP

Chapter 11: Linear Regression

Lecture 1: Introduction to the Dataset

Lecture 2: Preprocessing

Lecture 3: Linear Regression

Lecture 4: Metrics

Lecture 5: Cross Validation

Chapter 12: Ridge Regression

Lecture 1: Ridge Regression and Cross Validation

Chapter 13: Lasso Regression

Lecture 1: Lasso Regression and Cross Validation

Chapter 14: Regression – Understand the Models

Lecture 1: Analysis

Lecture 2: Data Scaling

Lecture 3: One-Hot Encoding

Lecture 4: Regularization

Lecture 5: Final Results

Chapter 15: Classification

Lecture 1: Introduction to the Dataset

Lecture 2: Partition of the Dataset: Train and Test

Lecture 3: Preprocessing

Lecture 4: Principal Component Analysis

Lecture 5: Linear Discriminant Analysis

Lecture 6: Naive Bayes Classifier

Lecture 7: Quadratic Classifier

Lecture 8: Logistic Regression

Chapter 16: Support Vector Machines for Regression

Lecture 1: Introduction to Support Vector Machines

Lecture 2: Introduction to the Dataset

Lecture 3: Partition of the Dataset – Target Variable

Lecture 4: Partition of the Dataset – Time Series Windows

Lecture 5: Support Vector Machine – Linear Kernel

Lecture 6: Support Vector Machines – Polynomial Kernels

Lecture 7: Support Vector Machine – Radial Basis Function (RBF) Kernel

Chapter 17: Support Vector Machines for Classification

Lecture 1: Introduction to Support Vector Machines

Lecture 2: Introduction to the Dataset

Lecture 3: Partition of the Dataset

Lecture 4: Transformation to Data Matrix

Lecture 5: Dimensionality Reduction

Lecture 6: Support Vector Machine – Linear Kernel

Lecture 7: Support Vector Machines – Polynomial Kernels

Lecture 8: Support Vector Machine – Radial Basis Function (RBF) Kernel

Chapter 18: Neural Networks

Lecture 1: Introduction to Neural Networks

Chapter 19: Neural Networks for Regression

Lecture 1: Dataset Information

Lecture 2: Introduction to the Dataset

Lecture 3: Partition of the Dataset – Target Variable

Lecture 4: Partition of the Dataset – Time Series Windows

Lecture 5: Multilayer Perceptron Neural Network

Chapter 20: Neural Networks for Classification

Lecture 1: Introduction to the Dataset

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 0 votes
  • 3 stars: 9 votes
  • 4 stars: 12 votes
  • 5 stars: 26 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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