Step by Step Guide to Machine Learning $20 Udemy Courses Free Now On Freewebcart.com Limited Offer Enroll Now. Only 2 days left
Course Provider: Organization
Step by Step Guide to Machine Learning $20 Udemy Courses Free Now On Freewebcart.com Limited Offer Enroll Now. Only 2 days left
Udemy Course Name |
Step by Step Guide to Machine Learning |
Publisher |
EdYoda Digital University |
Price | $20 |
Course Language | English |
If you are looking to start your career in machine learning then this is the course for you.
This is a course designed in such a way that you will learn all the concepts of machine learning right from basic to advanced levels.
This course has 5 parts as given below:
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Machine Learning Basics Logistic Regression LDA KNN in R $30 Udemy Courses Free Now On Freewebcart.com Limited Offer Enroll Now. Only 2 days left
Course Provider: Organization
Machine Learning Basics Logistic Regression LDA KNN in R $30 Udemy Courses Free Now On Freewebcart.com Limited Offer Enroll Now. Only 2 days left
Udemy Course Name |
Machine Learning Basics Logistic Regression LDA KNN in R |
Publisher |
Start-Tech Academy |
Price | $30 |
Course Language | English |
You’re looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in R, right?
You’ve found the right Classification modeling course covering logistic regression, LDA and kNN in R studio!
After completing this course, you will be able to:
· Identify the business problem which can be solved using Classification modeling techniques of Machine Learning.
· Create different Classification modelling model in R and compare their performance.
· Confidently practice, discuss and understand Machine Learning concepts
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular Classification techniques of machine learning, such as Logistic Regression, Linear Discriminant Analysis and KNN
This course covers all the steps that one should take while solving a business problem using classification techniques.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Machine Learning Basics Logistic Regression LDA KNN in R
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.
Below are the course contents of this course on Linear Regression:
This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation Machine Learning Basics Logistic Regression LDA KNN in R
This section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R.
In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.
We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.
This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors. Machine Learning Basics Logistic Regression LDA KNN in R
We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a classification model in R will soar. You’ll have a thorough understanding of how to use Classification modelling to create predictive models and solve business problems.
Go ahead and click the enroll button, and I’ll see you in lesson 1!
Cheers
Start-Tech Academy
Below is a list of popular FAQs of students who want to start their Machine learning journey-
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine Learning Basics Logistic Regression LDA KNN in R
In this course we learn both parametric and non-parametric classification techniques. The primary focus will be on the following three techniques:
Classification is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn classification starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of classification. Machine Learning Basics Logistic Regression LDA KNN in R
You can divide your learning process into 3 parts:
Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning – Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Machine Learning Basics Logistic Regression LDA KNN in R
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Machine Learning Basics Logistic Regression LDA KNN in R
Complete Python Bootcamp for Data Science Machine Learning $20 Udemy Courses Free Now On Freewebcart.com Limited Offer Enroll Now. Only 2 days left
Course Provider: Organization
Complete Python Bootcamp for Data Science Machine Learning $20 Udemy Courses Free Now On Freewebcart.com Limited Offer Enroll Now. Only 2 days left
Udemy Course Name |
Complete Python Bootcamp for Data Science Machine Learning |
Publisher |
Module- AI |
Price | $20 |
Course Language | English |
Are you ready to start your path to becoming a Data Scientist!
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost!
Enroll in the course and become a data scientist today!
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Machine Supervised Learning Regression in Python 3 and Math $200 Udemy Courses Free Now On Freewebcart.com Limited Offer Enroll Now. Only 1 day left
Course Provider: Organization
Machine Supervised Learning Regression in Python 3 and Math $200 Udemy Courses Free Now On Freewebcart.com Limited Offer Enroll Now. Only 1 day left
Udemy Course Name |
Machine Supervised Learning Regression in Python 3 and Math |
Publisher |
Ahmed Attia |
Price | $200 |
Course Language | English |
Artificial Intelligence has become prevalent recently. People across different disciplines are trying to apply AI to make their tasks a lot easier. For example, economists are using AI to predict future market prices to make a profit, doctors use AI to classify whether a tumor is malignant or benign, meteorologists use AI to predict the weather, HR recruiters use AI to check the resume of applicants to verify if the applicant meets the minimum criteria for the job, etcetera.
The impetus behind such ubiquitous use of AI is machine learning algorithms. For anyone who wants to learn ML algorithms but hasn’t gotten their feet wet yet, you are at the right place. The rudimental algorithm that every Machine Learning enthusiast starts with is a linear regression algorithm. Therefore, we shall do the same as it provides a base for us to build on and learn other ML algorithms.
Before knowing what is linear regression, let us get ourselves accustomed to regression. Regression is a method of modeling a target value based on independent predictors. This method is mostly used for forecasting and finding out the cause and effect relationship between variables. Regression techniques mostly differ based on the number of independent variables and the type of relationship between the independent and dependent variables.
Want to learn more about regression? Don’t hesitate and join us to begin the journey of learning!