What you’ll learn
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Python Programming Basics For Data Science
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Machine Learning – [A -Z] Comprehensive Training with Step by step guidance
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Supervised Learning – (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)
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Unsupervised Learning – Clustering, K-Means clustering
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Evaluating the Machine Learning Algorithms : Precision, Recall, F-Measure, Confusion Matrices,
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Data Pre-processing – Data Preprocessing is that step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it.
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Algorithm Analysis For Data Scientists
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KERAS Tutorial – Developing an Artificial Neural Network in Python -Step by Step
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Deep Learning -Handwritten Digits Recognition [Step by Step] [Complete Project ]
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Deep Convolutional Generative Adversarial Networks (DCGAN)
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Java Programming For Data Scientists
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Kaggle – Covid 19- Classification (Chest X-ray.) – Covid-19 & Pneumonia
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Developing a CNN From Scratch for CIFAR-10 Photo Classification
Who this course is for:
- Anyone who wish to start the career in Data Science
Can I download Professional Certificate in Data Science course?
You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Professional Certificate in Data Science course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.Are there any other coupons available for this course?
You can check out for more Udemy coupons @ www.coursecouponclub.com
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Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.