| 001. Course Introduction .mp4 | 14 MB | ||
| 001. Course Introduction en.srt | 1.2 KB | ||
| 001. Introduction to Module 1 en.srt | 1.6 KB | ||
| 001. Introduction to Module 1.mp4 | 5.7 MB | ||
| 001. Introduction to Module 2 en.srt | 2.3 KB | ||
| 001. Introduction to Module 2.mp4 | 6.1 MB | ||
| 001. Introduction to Module 3 en.srt | 3 KB | ||
| 001. Introduction to Module 3.mp4 | 10.5 MB | ||
| 001. Introduction to Module 4 en.srt | 2.6 KB | ||
| 001. Introduction to Module 4.mp4 | 9.7 MB | ||
| 001. Introduction to Module 5 en.srt | 2.3 KB | ||
| 001. Introduction to Module 5.mp4 | 8.7 MB | ||
| 001. Module 6 Introduction en.srt | 921.6 B | ||
| 001. Module 6 Introduction.mp4 | 3.7 MB | ||
| 002. Descriptive Statistics en.srt | 23.8 KB | ||
| 002. Descriptive Statistics.mp4 | 89 MB | ||
| 002. Logistic Regression Basics en.srt | 13.3 KB | ||
| 002. Logistic Regression Basics.mp4 | 44.4 MB | ||
| 002. Mathematical Functions en.srt | 25.1 KB | ||
| 002. Mathematical Functions.jpeg | 159.7 KB | ||
| 002. Mathematical Functions.mp4 | 101.8 MB | ||
| 002. Simple Linear Regression en.srt | 20.9 KB | ||
| 002. Simple Linear Regression.mp4 | 61.6 MB | ||
| 002. The Monty Hall Problem en.srt | 7.3 KB | ||
| 002. The Monty Hall Problem.mp4 | 21 MB | ||
| 002. Vectors and Vector Operations en.srt | 22.5 KB | ||
| 002. Vectors and Vector Operations.mp4 | 58.8 MB | ||
| 003. Exponential and Logarithmic Functions en.srt | 11 KB | ||
| 003. Exponential and Logarithmic Functions.mp4 | 27.3 MB | ||
| 003. Fitting a Logistic Regression en.srt | 20 KB | ||
| 003. Fitting a Logistic Regression.mp4 | 69.3 MB | ||
| 003. Multiple Linear Regression en.srt | 5.5 KB | ||
| 003. Multiple Linear Regression.mp4 | 25.4 MB | ||
| 003. Probability Basics en.srt | 24.9 KB | ||
| 003. Probability Basics.mp4 | 104.9 MB | ||
| 003. The Normal Distribution en.srt | 14.5 KB | ||
| 003. The Normal Distribution.mp4 | 41.4 MB | ||
| 003. Transformations and Matrices en.srt | 21.5 KB | ||
| 003. Transformations and Matrices.mp4 | 65.4 MB | ||
| 004. Bayes Theorem en.srt | 11.7 KB | ||
| 004. Bayes Theorem.mp4 | 40.7 MB | ||
| 004. Fitting a Linear Regression en.srt | 28.9 KB | ||
| 004. Fitting a Linear Regression.mp4 | 100.3 MB | ||
| 004. The Central Limit Theorem en.srt | 5.5 KB | ||
| 004. The Central Limit Theorem.mp4 | 17.1 MB | ||
| 004. The Limit and the Derivative en.srt | 29.1 KB | ||
| 004. The Limit and the Derivative.mp4 | 94 MB | ||
| 004. The Log-Odds en.srt | 7.2 KB | ||
| 004. The Log-Odds.mp4 | 21.1 MB | ||
| 004. Transformations, Matrices, and Matrix Multiplication en.srt | 6.3 KB | ||
| 004. Transformations, Matrices, and Matrix Multiplication.mp4 | 18.4 MB | ||
| 005. Binomial and Beta Distribution en.srt | 13.7 KB | ||
| 005. Binomial and Beta Distribution.mp4 | 41.8 MB | ||
| 005. Integrals en.srt | 5.9 KB | ||
| 005. Integrals.mp4 | 14.8 MB | ||
| 005. Overfitting, Variance, and RidgeLasso Regression en.srt | 15.6 KB | ||
| 005. Overfitting, Variance, and RidgeLasso Regression.mp4 | 52 MB | ||
| 005. Systems of Linear Equations and Inverse Matrices en.srt | 9.9 KB | ||
| 005. Systems of Linear Equations and Inverse Matrices.mp4 | 26.9 MB | ||
| 005. The R2 and P-Value en.srt | 9.4 KB | ||
| 005. The R2 and P-Value.mp4 | 27.9 MB | ||
| 005. Z Scores and Confidence Intervals en.srt | 8.3 KB | ||
| 005. Z Scores and Confidence Intervals.mp4 | 30.5 MB | ||
| 006. Hypothesis Testing en.srt | 14.2 KB | ||
| 006. Hypothesis Testing.mp4 | 45.3 MB | ||
| 006. Matrix Decomposition en.srt | 8.4 KB | ||
| 006. Matrix Decomposition.mp4 | 27.6 MB | ||
| 006. ROCAUC and Confusion Matrices en.srt | 20.4 KB | ||
| 006. ROCAUC and Confusion Matrices.mp4 | 70.8 MB | ||
| 006. TrainTest Splits en.srt | 9.3 KB | ||
| 006. TrainTest Splits.mp4 | 31.1 MB | ||
| 007. Performance Metrics en.srt | 17.8 KB | ||
| 007. Performance Metrics.mp4 | 53.4 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 75 total files | |||
Applied Math for Data Science
https://DevCourseWeb.com
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.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 343 MB | freecoursewb | 10 months | 7 | 5 | |
| 4 GB | freecoursewb | 3 years | 3 | 1 |
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