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This plan includes
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HD Video Recorded Lectures- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
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- Email & Chat Support
- Cancel 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
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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
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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
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Section 3 : Logistic regression
- Lecture 1 :
- Logistic regression
- Lecture 2 :
- pratical exerice - Heart disease analysis using logistic regression
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Section 4 : KNN Algorithm
- Lecture 1 :
- KNN Algorithm
- Lecture 2 :
- Practical exercise using KNN Algorithm for Tumor classification
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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
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Section 7 : decision tree algorithm
- Lecture 1 :
- decision tree algorithm
- Lecture 2 :
- Practical example using decision tree algorithm
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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
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