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2021 Machine Learning Classification Models

LeeAndro

Trusted Editor
Trusted Editor
2021 Machine Learning Classification Models
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 47 lectures (3h 6m) | Size: 922.2 MB

Jupyter files that contain live codes, simulations and visualizations that experts use.


Top 7 CLASSIFICATION Models You Must Know in 2021

Overview of Classification and its Applications

Build Classification models using Logistic regression in Scikit-learn

Build and visualise K-Nearest Neighbours KNN Classification models on Kaggle data sets

Build Support Vector Machines SVM Classification models

Build Naive Bayes Classification models and compare with SVM models

Build and visualise Decision Tree Classification models and compare with other models

Build and visualise Random Forest Classification models and compare with other Decision Tree model

Build Multilayer Perceptron Classification models

The Final Classification Showdown

Action steps after every module that is similar to real-life projects

Advanced lessons that are not included in most deep learning courses out there

Apply your new-found knowledge through the Capstone project

Basic Python programming experience

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Now, this doesn't mean they can't catch up to you.

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Our Course is for:

ASPIRING DEVELOPERS - who want to improve their skills without wasting so much searching for answers on internet.

BUSINESS ANALYSTS - who want to become better in making data-driven decisions.

STARTUP TECHNOPRENEURS - who want to become better in machine learning and data science.

If you're any of these, then this course is designed to help you in the easiest and most efficient way possible.

Pre-requisite:

Basic Python programming experience.

Here's What You'll Learn Through Our Course:

Overview of Classification and its Applications

Build Classification models using Logistic regression in Scikit-learn

Build and visualise K-Nearest Neighbours KNN Classification models on Kaggle data sets

Build Support Vector Machines SVM Classification models

Build Naive Bayes Classification models and compare with SVM models

Build and visualise Decision Tree Classification models and compare with other models

Build and visualise Random Forest Classification models and compare with other Decision Tree model

Build Multilayer Perceptron Classification models

The Final Classification Showdown

Action steps after every module that is similar to real-life projects

Advanced lessons that are not included in most machine learning courses out there

Apply your new-found knowledge through the Capstone project

You also get these exciting FREE bonuses !

Bonus #1: Big Insider Secrets

These are industry secrets that most experts don't share without getting paid for thousands of dollars. These include how they successfully debug and fix projects that are usually dead-end, or how they successfully launch a machine-learning program.

Bonus #2: 4 Advanced Lessons

We will teach you the advanced lessons that are not included in most machine learning courses out there. It contains shortcuts and programming "hacks" that will make your life as a machine learning developer easier.

Bonus #3: Solved Capstone Project

You will be given access to apply your new-found knowledge through the capstone project. This ensures that both your mind and body will remember all the things that you've learned. After all, experience is the best teacher.

Bonus #4: 20+ Jupyter Code Notebooks

You'll be able to files that contain live codes, narrative text, numerical simulations, visualizations, and equations that you most experts use to create their own projects. This can help you come up with better codes that you can use to innovate within this industry.

ASPIRING DEVELOPERS - who want to improve their skills without wasting so much searching for answers on internet.

BUSINESS ANALYSTS - who want to become better in making data-driven decisions.

STARTUP TECHNOPRENEURS - who want to become better in machine learning and data science.



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