Open Source/Data Science and Machine Learning: Mathematical and Statistical Methods

Data Science and Machine Learning: Mathematical and Statistical Methods Certification Training Course Overview

AI and information examination are the focal point of fascination for some designers and researchers. The explanation is very self-evident: its tremendous application in various fields and blasting vocation alternatives. What's more, Python is one of the main open source stages for information science and numerical figuring. IPython, and its related Jupyter Notebook, furnish Python with effective interfaces to for information examination and intuitive perception, and they comprise a perfect portal to the stage. On the off chance that you are among those trying to improve their abilities in AI, at that point this course is the correct decision.

Factual Methods and Applied Mathematics in Data Science gives some simple to-follow, prepared to-utilize, and centered plans for information examination and logical registering. This course handles information science, insights, AI, sign and picture preparing, dynamical frameworks, and unadulterated and applied arithmetic. You will apply best in class techniques to different genuine models, delineating subjects in applied science, logical demonstrating, and AI. To put it plainly, you will be knowledgeable with the standard strategies in information science and scientific demonstrating.

Audience :

This course is planned for anybody inspired by AI and information science: understudies, scientists, instructors, specialists, investigators, and specialists.

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Data Science and Machine Learning: Mathematical and Statistical Methods (24 Hours) Download Course Contents

Live Virtual Classroom
Group Training 1200
01 - 03 Nov GTR 09:00 AM - 05:00 PM CST
(8 Hours/Day)
06 - 08 Dec 09:00 AM - 05:00 PM CST
(8 Hours/Day)
1-on-1 Training (GTR) 1350
4 Hours
8 Hours
Week Days
Weekend

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12 PM

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Course Modules

Module 1: Overview
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Module 2: Mean
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Module 3: Weighted Mean
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Module 4: Mode
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Module 5: Median
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Module 6: Weighted Median
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Module 7: Trimmed Mean
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Module 8: Outliers
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Module 9: Anomaly Detection
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Module 10: Deviations
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Module 11: Variance
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Module 12: Standard Deviation
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Module 13: Percentiles
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Module 14: Interquartile Range
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Module 15: BoxPlot Analysis
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Module 16: Histogram
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Module 17: Correlation coefficient.
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Module 18: Scatterplot
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Module 19: Categorical and numeric data
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Module 20: Sampling
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Module 21: Bias
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Module 22: Sample and Resample
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Module 23: Normal Distribution
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Module 24: A/B Testing
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Module 25: Hypothesis Test
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Module 26: Permutation
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Module 27: P-Value
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Module 28: Regression
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Module 29: RMSA
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Module 30: Classification
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Module 31: NaiveBayes
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Module 32: Confusion Matrix
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Module 33: ROC Curve
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Module 34: K-Nearest Neighbors
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Module 35: Hyper Parameter
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Module 36: K-means Clustering
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Course Prerequisites
  • Basic Computer Knowledge.

After completion of this course, you will learn how to :

  • Master all Jupyter Notebook highlights
  • Visualize information and make intuitive plots in Jupyter Notebook
  • Analyze information with Bayesian or frequentist insights (Pandas, PyMC, and R), and gain from genuine information through AI (scikit-learn)
  • Gain important experiences into signs, pictures, and sounds with SciPy, scikit-picture, and OpenCV
  • Simulate deterministic and stochastic dynamical frameworks in Python
  • Familiarize yourself with math in Python utilizing SymPy and Sage: variable based math, examination, rationale, diagrams, geometry, and likelihood hypothesis

 

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