Ensemble Machine Learning in Python: Random Forest, AdaBoost

Name: Ensemble Machine Learning in Python: Random Forest, AdaBoost
Brand: Udemy
SKU: 44337
Price: 15.99 USD
Availability: InStock

$15.99Track price

Lazy Programmer Inc. 26 Machine Learning Udemy

Add your review

Description
Instructor Details
Additional information
Reviews (2)

8.5/10 (Our Score)

Product is rated as #130 in category Machine Learning

In recent years, we’ve seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on–par with human experts.

Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now machine learning first , and companies like NVIDIA and Amazon have followed suit, and this is what’s going to drive innovation in the coming years.

Machine learning is embedded into all sorts of different products, and it’s used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you’re in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

Instructor Details

Lazy Programmer Inc.Artificial intelligence and machine learning engineer

Votes: 1

Courses : 22

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Specification: Ensemble Machine Learning in Python: Random Forest, AdaBoost

Duration	5.5 hours
Year	2020
Level	All
Certificate	Yes
Quizzes	No

2 reviews for Ensemble Machine Learning in Python: Random Forest, AdaBoost

5.0 out of 5

★★★★★

Write a review

Show all Most Helpful Highest Rating Lowest Rating

★★★★★
Naveen Jawaid – January 19, 2020
Very good and challenging course on statistical learning theory of ensembles. The instructor makes things easier than a boring dry textbook with extensive examples and illustrations. It was a great learning experience.
Helpful(0) Unhelpful(0)You have already voted this
★★★★★
Hyunsik Shin – January 30, 2020
It touches the meaning of the techniques(for example, bootstrap, bagging, …) and it shows a concrete examples with code inside.
Helpful(0) Unhelpful(0)You have already voted this