Tag: Regression

SKILLS YOU WILL GAIN

• Data Clustering Algorithms
• Machine Learning
• Classification Algorithms
• Decision Tree
• Python Programming
• Machine Learning Concepts
• Deep Learning
• Linear Regression
• Ridge Regression
• Lasso (Statistics)
• Regression Analysis
• Logistic Regression

About this Specialization

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

Applied Learning Project

Learners will implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real datasets throughout each course in the specialization. They will walk away with applied machine learning and Python programming experience.

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You’ll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you’ll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.

WHAT YOU WILL LEARN

• Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
• Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
• Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
• Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model

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SKILLS YOU WILL GAIN

• Decision Trees
• Artificial Neural Network
• Logistic Regression
• Recommender Systems
• Linear Regression
• Regularization to Avoid Overfitting
• Gradient Descent
• Supervised Learning
• Logistic Regression for Classification
• Xgboost
• Tensorflow
• Tree Ensembles

About this Specialization

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. 

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. 

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Applied Learning Project

By the end of this Specialization, you will be ready to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.

• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.

• Build and train a neural network with TensorFlow to perform multi-class classification.

• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.

• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.

• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.

• Build a deep reinforcement learning model.

Master K-Nearest Neighbors in Python

Become an advanced, confident, and modern data scientist from scratch

Become job-ready by understanding how KNN really works behind the scenes

Apply robust Data Science techniques for the K-Nearest Neighbors algorithm

Solve Machine Learning Prediction Problems using KNN

How to think and work like a data scientist: problem-solving, researching, workflows

Get fast and friendly support in the Q&A area

Requirements

• No data science experience is necessary to take this course.
• Any computer and OS will work — Windows, macOS or Linux. We will set up your code environment in the course.

Description

You’ve just stumbled upon the most complete, in-depth KNN for Regression 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 KNN to be able to create your own projects quickly.

…this complete K-Nearest Neighbors for Regression Masterclass is the course you need to do all of this, and more.

This course is designed to give you the KNN skills you need to become a data science expert. By the end of the course, you will understand the K-Nearest Neighbors for Regression method extremely well and be able to apply them 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 K-Nearest Neighbors for Regression 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 dimensionality reduction skills and master the K-Nearest Neighbors technique. It’s a one-stop shop to learn Multilayer Networks. 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.

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 an extra, this course includes Python code templates which you can download and use on your own projects.

Ready to get started, developer?

Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced Multilayer Networks 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, K-Nearest Neighbors is waiting!)

Who this course is for:

• Any people who want to start learning K-Nearest Neighbors in Data Science
• Anyone interested in Machine Learning
• Anyone who want to understand how to use K-Nearest Neighbors in datasets using Python

Course content

Master Support Vector Machines for Regression in Python

Become an advanced, confident, and modern data scientist from scratch

Become job-ready by understanding how Support Vector Machines really work behind the scenes

Apply robust Data Science techniques for Support Vector Machines

How to think and work like a data scientist: problem-solving, researching, workflows

Get fast and friendly support in the Q&A area

Requirements

• No data science experience is necessary to take this course.
• Any computer and OS will work — Windows, macOS or Linux. We will set up your code environment in the course.

Description

You’ve just stumbled upon the most complete, in-depth Support Vector Machines for Regression 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 SVM to be able to create your own projects quickly.

…this complete Support Vector Machines for Regression Masterclass is the course you need to do all of this, and more.

This course is designed to give you the Support Vector Machine skills you need to become a data science expert. By the end of the course, you will understand the SVM 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 Support Vector Machines for Regression 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 dimensionality reduction skills and master the SVM technique. It’s a one-stop shop to learn SVM. 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.

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 templates which you can download and use on your own projects.

Ready to get started, developer?

Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced SVM 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, Support Vector Machines are waiting!)

Who this course is for:

• Any people who want to start learning Support Vector Machines in Data Science
• Anyone interested in Machine Learning
• Anyone who want to understand how to apply Support Vector Machines in datasets using Python

Course content

Learn to perform Classification and Regression modelling

Master Machine Learning and use it on the job

Use Seaborn to create beautiful statistical plots with Python.

Get set-up quickly with the Anaconda data science stack environment.

Requirements

• There is no specific prerequisite to learn machine learning. But you need to be from engineering/science/Maths/Stats background to understand the theory and the techniques used. You need to be good in mathematics. If you are not, still you can machine learning, but you will face difficulty when solving complex real world problems. Many say you need to know Linear algebra, Calculus etc. etc. but I never learnt it, yet I am able to work on machine learning.

Description

In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku).

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.

More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.

In This Course, We Are Going To Work On 2 Real World Projects Listed Below:

Project-1: Toxic Comment Classification

Project-2: UK_Road_Accident_Timeseries_Forecasting

The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career

Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Free

Who this course is for:

• Beginners in machine learning
• Career transition from Non Technical into Data Science
• Fresher to get job into Machine Learning Engineer
• Beginner into Machine Learning

Course content

Understand the concept of regression analysis and its applications in predictive modeling.

Understand the concepts of overfitting and underfitting.

Learn how to build a regression tree using Minitab.

Learn how to set up a binary logistic regression model using Minitab.

Practice building a classification tree and using it for prediction using Minitab.

Requirements

• No prior programming knowledge is required. The tutorials are based on Minitab software version 21. If you want to try it yourself on the data files provided, you will need this software. The 30-day trial is free. The course assumes basic statistical knowledge.

Description

In this mini-course, “Tasting Machine Learning with Minitab Predictive Analytics”, you will gain an introduction to the world of predictive analytics and machine learning using Minitab statistical software.

Through five lectures, you will learn about regression analysis and classification, two fundamental techniques in predictive modeling. In the first two lectures, you will learn how to set up and verify regression models, as well as how to identify and address potential issues with overfitting or underfitting. In Lecture 3, you will explore regression trees, which are a powerful alternative to linear regression when the relationship between variables is non-linear.

In Lecture 4, you will delve into binary logistic regression, which is a technique used for predicting binary outcomes (such as “yes” or “no” responses). You will learn how to set up and evaluate a binary logistic regression model. Finally, in Lecture 5, you will discover classification trees, which are a type of decision tree used to classify objects or cases into different categories. You will learn how to build and interpret classification trees, and use them for prediction.

By the end of this mini-course, you will have gained practical experience in building and evaluating regression and classification models using Minitab, and an understanding of how these techniques can be applied in various real-world scenarios. Whether you are new to machine learning or looking to expand your knowledge, this mini-course is an excellent opportunity to explore the basics of predictive analytics with Minitab.

Who this course is for:

• This course is for those who want a concise taste of the 4 basic methods of machine learning before embarking on a more detailed course.

Course content

Theoretical concept of Machine learning

Concepts of Regression

Concepts of data preprocessing

Overall Machine Learning theory and its algorithms

Requirements

• None. You will learn everything from its basic

Description

The course introduces the concepts of Machine Learning.  It covers the regression, both linear and logistic. Decision trees, Data preprocessing etc., to explore the machine learning in brief with plenty of examples. it also discusses the concept of state space, bias, etc. in Machine learning. Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. This course covers the following aspects of machine learning:

1.Basic Definitions 2.Types of Learning

3.Hypothesis space and Inductive Bias

4.Evaluation

5.Cross-Validation

6.Linear Regression

7.Decision Trees 8.Overfitting

The course is designed after studying the syllabus of various technological universities. Machine learning is basically of three types: The main goal in supervised learning is to learn a model from labeled training data that allows us to make predictions about unseen or future data. Supervised refers to a set of samples where the desired output signals (labels) are already known.

Unsupervised learning deals with unlabeled data or data of unknown structure. Using unsupervised learning techniques explores the structure of our data to extract meaningful information without the guidance of a known outcome variable or reward function.

In reinforcement learning, the goal is to develop a system (agent) that improves its performance based on interactions (reward signals) with the environment.

Who this course is for:

• The course is intended for the students of BCA, BE, BTech and anybody interested in Machine Learning

Course content

An overview of the workflow from starting to launching an ML project

Essential terms that will pop up often during ML conversations

Overview of classification and regression goals

Understanding of some of the techniques you can use to optimize your ML model

Requirements

• Curiosity about the machine learning project structure

Description

Machine Learning has become an exciting route to go down by many teams and companies. However, it’s not always realistic that everyone is expected to catch up with all of the latest ML trends.

Usually Machine learning teams are made up of different people. On the technical side you can have a mixture of the different data scientists and engineers, like a Machine Learning Data Scientists, as well as Machine Learning and Data Engineers. The data scientists’ main responsibility would be building out or improving the models, and the engineers will help with everything else around deployment and that the models are getting the data they need.

From the non-technical side it’s likely you’ll have a project manager and possibly also several other business stakeholders. This course is aimed for these people, who need to understand what’s going on at a higher level, without necessarily having to dive into the technical components. Those that need to know enough to help with product vision, and be able to have and understand discussions about current statuses, blockers, as well as estimations.

In this course we’ll look at some of the different components involved in an ML project so that you can feel like you can have fruitful conversations when working on an ML project without needing to get bogged up on all the technical details.

Who this course is for:

• Anyone who wants to get a high-level overview of the different components involved in machine learning

Course content

Machine Learning

Python

Regression

Classification

Unsupervised Machine Learning

Requirements

• Zero prior technical experience is required! All you need is a passion to learn and experiment new things.

Description

This course will be a part of series of Free ML Courses to become an expert of ML. Presenting here its First Course on Machine Learning for becoming expert of ML.

This course presents the concepts of Supervised Machine Learning, Unsupervised Machine Learning, Regression and Classification.

It covers implementation of Simple Linear Regression.

Who this course is for:

• Machine Learning Enthusiast
• Students taking Machine Learning Course
• Professionals working in the area of Data Analytics
• Students preparing for placement tests and interviews
• Excellent course for all the students of Non-IT Branches as it provides the basic knowledge of ML without any prerequisite

Course content

Students will get an opportunity to explore the complex mathematics behind machine learning algorithms.

Students will be able to write machine learning algorithms from scratch.

Students will get guidance on how to build in the knowledge they gained in the course.

Student will be receive life-long access to the course for future reference.

Requirements

• Students should be well-versed with calculus and should know basic python syntax.

Description

This course is for students who are looking for logic behind the myriad of machine learning algorithms they use every day. When I started my journey with machine learning, it was really difficult for me to intuitively understand the code I was writing. However, after watching multiple videos and reading millions of articles, I finally understood the fundamentals of machine learning algorithms. In this course, I’ll walk you through the mathematical concepts you need to know to understand and implement a machine learning algorithm. Other than that. you’ll also learn how to build the same algorithms from scratch using python. No kind of libraries will be imported during the course. This will help you in understanding the algorithm properly as none of the work will be taking place in the background. This course does not feature high-level machine algorithms instead it focuses on the most basic ones: bivariate regression, multivariate regression, support vector regression, k-nearest neighbors. The scope of this course will gradually expand and soon it will feature tutorials on techniques like deep neural networks. This course is a condensed version of my knowledge which I gained through multiple resources. You are free to drop in your queries in the Q&A section, I will be glad to resolve them. Happy coding 😉

Who this course is for:

• High-schoolers and freshmen with an urge to explore machine learning.

Course content