Applied Math for Data Science

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Applied Math for Data Science
Language: English | Size:1.45 GB
Genre:eLearning
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Files Included :

Spoiler

001 Course Introduction.mp4 (13.98 MB)
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001 Introduction to Module 1.mp4 (5.67 MB)
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002 Mathematical Functions.mp4 (101.82 MB)
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003 Exponential and Logarithmic Functions.mp4 (27.29 MB)
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004 The Limit and the Derivative.mp4 (94.01 MB)
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005 Integrals.mp4 (14.76 MB)
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001 Introduction to Module 2.mp4 (6.12 MB)
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002 The Monty Hall Problem.mp4 (21.01 MB)
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003 Probability Basics.mp4 (104.9 MB)
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004 Bayes Theorem.mp4 (40.68 MB)
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005 Binomial and Beta Distribution.mp4 (41.76 MB)
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001 Introduction to Module 3.mp4 (10.55 MB)
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002 Descriptive Statistics.mp4 (89.02 MB)
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003 The Normal Distribution.mp4 (41.38 MB)
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004 The Central Limit Theorem.mp4 (17.09 MB)
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005 Z Scores and Confidence Intervals.mp4 (30.52 MB)
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006 Hypothesis Testing.mp4 (45.27 MB)
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001 Introduction to Module 4.mp4 (9.67 MB)
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002 Vectors and Vector Operations.mp4 (58.77 MB)
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003 Transformations and Matrices.mp4 (65.4 MB)
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004 Transformations, Matrices, and Matrix Multiplication.mp4 (18.38 MB)
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005 Systems of Linear Equations and Inverse Matrices.mp4 (26.85 MB)
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006 Matrix Decomposition.mp4 (27.57 MB)
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001 Introduction to Module 5.mp4 (8.68 MB)
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002 Simple Linear Regression.mp4 (61.61 MB)
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003 Multiple Linear Regression.mp4 (25.41 MB)
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004 Fitting a Linear Regression.mp4 (100.25 MB)
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005 Overfitting, Variance, and RidgeLasso Regression.mp4 (52.04 MB)
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006 TrainTest Splits.mp4 (31.06 MB)
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007 Performance Metrics.mp4 (53.35 MB)
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001 Module 6 Introduction.mp4 (3.67 MB)
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002 Logistic Regression Basics.mp4 (44.45 MB)
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003 Fitting a Logistic Regression.mp4 (69.25 MB)
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004 The Log-Odds.mp4 (21.06 MB)
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005 The R2 and P-Value.mp4 (27.89 MB)
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006 ROCAUC and Confusion Matrices.mp4 (70.83 MB)
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[0dayddl]

Applied Math for Data Science
MP4 | Video: h264, yuv420p, 1920x1080 | Audio: aac, 44100 Hz | Duration: 5h 41m | 1.45 GB
Genre: eLearning | Language: English​

With the availability of data, there is a growing demand for talent who can analyze and make sense of it. This makes practical math all the more important because it helps infer insights from data. However, mathematics comprises many topics, and it is hard to identify which ones are applicable and relevant for a data science career. Knowing these essential math topics is key to integrating knowledge across data science, statistics, and machine learning.

In this course, learners will delve into a carefully curated list of mathematical topics to jumpstart proficiency in areas of mathematics that they will be able to apply immediately. They will grasp the fundamentals of probability, statistics, hypothesis testing, linear algebra, linear regression, classification models, and practical calculus. Along the way they will integrate this knowledge into practical applications for real-world problems.

What you'll learn and how you can apply it
Gain a fundamental grasp of calculus, linear algebra, probability, statistics, and supervised machine learning.
Apply mathematical fundamental principles in Python using standard mathematical libraries like NumPy and SymPy.
Integrate multiple applied mathematical disciplines like linear algebra and calculus to perform tasks like gradient descent.
This course is for you because.
You're a budding data science professional who wants to build foundational knowledge in essential math concepts and how they apply to probability, statistics, and machine learning.
You're a programmer using data science and machine learning libraries and want to understand the math and probability principles behind them.
You're managing a data science team and want to have a fundamental understanding of techniques used on the field.

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