Tech & IT
Business
Coding & Developer
Finance & Accounting
Academics
Office Applications
Art & Design
Marketing
Health & Wellness
Sounds & Music
Lifestyle
Photography
This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
HD Video Recorded Lectures- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
This plan includes
- Instant access to 11,000+ online courses
- Play & Pause Course Videos
- HD Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Instructor Chat Support
- Cancel Plan Anytime
What you'll learn?
- Learning will get expertise in Machine learning and he will be able to work as data scientest/ ML engineer.
Course Overview
This course will cover following topics
1. Basics of machine learning
2. Supervised and unsupervised learning
3. Linear regression
4. Logistic regression
5. KNN Algorithm
6. Naïve Bayes Classifier
7. Random forest Algorithm
8. Decision Tree Algorithm
7. Principal component analysis
8. K-means clustering
9. Agglomerative clustering
10. There will practical exercise based on Linear regression, Logistic regression ,Naive Bayes, KNN algorithm, Random forest, Decision tree, K-Means, PCA .
11. There will be quiz for each topics and total 200 Questions on machine learning course
We will look first in to linear Regression, where we will learn to predict continuous variables and this will details of Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R-Squared and Adjusted R-Squared.
We will get full details of Logistic Regression, which is by far the most popular model for Classification. We will learn all about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios.... and you will build your very first Logistic Regression
We will look in to Naïve bias classifier which will give full details of Bayes Theorem, implementation of Naïve bias in machine learning. This can be used in Spam Filtering, Text analysis, •Recommendation Systems.
Random forest algorithm can be used in regression and classification problems. This gives good accuracy even if
data is incomplete.
Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems.
We will look in to KNN algorithm which will working way of KNN algorithm, compute KNN distance matrix, Makowski distance, live examples of implementation of KNN in industry.
We will look in to PCA, K-means clustering, Agglomerative clustering which will be part of unsupervised learning.
Along all part of machine supervised and unsupervised learning , we will be following data reading , data prerprocessing, EDA, data scaling, preparation of training and testing data along machine learning model selection , implemention and prediction of models.
Pre-requisites
- Person should know basic programming of Python. He should have laptop with good processing capacity along Anaconda software installed him laptop or disktop.
Target Audience
- The students should be engineering graduate preferable done engineering in computor science or electronics and communication engineering. Course is good for those who wants to be datas scientest/ Machine learning engineer.
Curriculum 19 Lectures 06:01:56
-
Section 1 : Basics of machine learning
- Lecture 2 :
- Supervised learning, Unsupervised learning , advantages and disadvantages of ML
- Lecture 3 :
- ML life cycle, Exploratory data analysis , ML Challenges and libraries
-
Section 2 : Linear Regression
- Lecture 1 :
- Linear and multiple linear regression, cost function, gradient decent method
- Lecture 2 :
- practical exercise - car price prediction model using linear regression
- Lecture 3 :
- Assumptions, Advantages and disadvantage, best practices, MAE, MAPE,MSE L regres
-
Section 3 : Logistic regression
- Lecture 1 :
- Logistic regression
- Lecture 2 :
- pratical exerice - Heart disease analysis using logistic regression
-
Section 4 : KNN Algorithm
- Lecture 1 :
- KNN Algorithm
- Lecture 2 :
- Practical exercise using KNN Algorithm for Tumor classification
-
Section 5 : Naïve Bayes Algorithm
- Lecture 1 :
- Naïve Bayes Algorithm
- Lecture 2 :
- Practical excerise using Navie Bayes for SPAMs
-
Section 6 : Random forest algorithm
- Lecture 1 :
- Random forest alorgthim
- Lecture 2 :
- Practical example using Random forest algorithm
-
Section 7 : decision tree algorithm
- Lecture 1 :
- decision tree algorithm
- Lecture 2 :
- Practical example using decision tree algorithm
-
Section 8 : Unsupervised learning
- Lecture 1 :
- Unsupervised learning , type of unsupervised learning, adv and disadvantages etc
-
Section 9 : PCA and live exercise on unsupervised learning.
- Lecture 1 :
- Principal component analysis
- Lecture 2 :
- live exercise on unsupervised learning
Our learners work at
Frequently Asked Questions
-
How do i access the course after purchase?
It's simple. When you sign up, you'll immediately have unlimited viewing of thousands of expert courses, paths to guide your learning, tools to measure your skills and hands-on resources like exercise files. There’s no limit on what you can learn and you can cancel at any time. -
Are these video based online self-learning courses?
Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic. -
Can i play & pause the course as per my convenience?
Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on. -
How do i contact the instructor for any doubts or questions?
Most of these courses have general questions & answers already covered within the course lectures. However, if you need any further help from the instructor, you can use the inbuilt Chat with Instructor option to send a message to an instructor & they will reply you within 24 hours. You can ask as many questions as you want. -
Do i need a pc to access the course or can i do it on mobile & tablet as well?
Brilliant question? Isn't it? You can access the courses on any device like PC, Mobile, Tablet & even on a smart tv. For mobile & a tablet you can download the Learnfly android or an iOS app. If mobile app is not available in your country, you can access the course directly by visting our website, its fully mobile friendly. -
Do i get any certificate for the courses?
Yes. Once you complete any course on our platform along with provided assessments by the instructor, you will be eligble to get certificate of course completion.
-
For how long can i access my course on the platform?
You require an active subscription to access courses on our platform. If your subscription is active, you can access any course on our platform with no restrictions. -
Is there any free trial?
Currently, we do not offer any free trial. -
Can i cancel anytime?
Yes, you can cancel your subscription at any time. Your subscription will auto-renew until you cancel, but why would you want to?
Instructor
3310 Course Views
23 Courses
