** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training **
This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial:
1. What is Classification?
2. Types of Classification
3. Classification Use Case
4. What is Decision Tree?
5. Decision Tree Terminology
6. Visualizing a Decision Tree
7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm
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#decisiontree #decisiontreepython #machinelearningalgorithms
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About the Course
Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present
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Why learn Machine Learning with Python?
Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Python Machine Learning Course curriculum, Visit our Website: http://bit.ly/2FBUtO7
Hi Abhishek, in practice, some form of cross-validation is typically applied. However, there are ways to make an informed pre-selection. You can go with a maximum margin classifier such as support vector machines. It can be considered the best off the shelf classifier to date.
Hey Jaber! Thank you for appreciating our efforts. You can check out our Complete Machine learning tutorial here: https://www.youtube.com/watch?v=b2q5OFtxm6A&list=PL9ooVrP1hQOHUfd-g8GUpKI3hHOwM_9Dn Hope this is helpful. Cheers!
Sir , I was assigned a project on fraud detection . Which algorithms should I learn to train many transactions and detect if a transaction is legitimate or fraud ?
I wanted to implement in Python .
Please guide me in this project .
Hey Manivarma, Classification and clustering algorithms are good for fraud detection and anomoly detection. So algorithms like, SVM, KNN, K-Means, Decison trees, Random forest are relevant.
Hope this helps!
Why didn't 'temperature' come into picture while constructing the decision tree? Is it because the information gain is least compared to all other nodes? if yes, then every time when we construct the decision tree should we ignore the parameter with least info gain?
Hey, great video but I have a question i am working with a data set that has 569 instances and 30 variables, problem is that the variables aren't like the example, they are not standard options, like shown in the video where outlook has 3 distinct options, these variables are all doubles, they range from 7.33 up to 22.45 or something like that, so i'm really not sure how to calculate entropy for that
Hey Sweta, "Formula for entropy is -P(YES)xlog2(P(YES)-P(NO)xlog2(P(NO)
Now since out of 14 instances, we have 9 Yes and 5 No. So put 9 and 5 in the formula above you will the entropy as 0.94. For a better explanation please jump to 26.12 minutes of this video."
Hope this helps!
Hey Raj, You can set the variable to r rangle. For example, if you have around 5000 tuples, then you can use just 200 tuples and then assign it to train data. After that you can use the algorithm and test the data based on the chosen tuples
The code is not there in the description .. can you get me that?
The video lecture was awesome..
I tried doing alongside..but have some errors. If given the sample code may be I can check it out.
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