Real-world, code-oriented learning for programmers to use probability/stats in all of CS, Data Science and Machine Learning!!
On Completion of this course, you’ll be able to understand:
- Necessary concepts in stats and probability
- Important concepts in the subject necessary for Data Science and/or ML
- Distributions and their importance
- Entropy – the foundation of all Machine Learning
- Intro to Bayesian Inference
This course is just apt for you in case you are:
- Beginner ML and data science developers who need a strong foundation
- Curious about data science and machine learning
- Looking to find out why probability is the foundation of all modern machine learning
- Wanting to know how to harness the power of big data
Some exceptional benefits associated with this course enrolment are:
- Quality course material on probability & statistics
- Lifetime access to the course
- Instant & free course updates
- Access to all Questions & Answers initiated by other students as well
- Personalized support from the instructor’s end on any issue related to the course
- Few free lectures for a quick overview
It’s time for you to grab the opportunity and make the most out of this course.
Enroll today!!
Curriculum
- 7 Sections
- 29 Lessons
- 7 Hours
- Diving in with Code6
- 2.1Code env setup and python crash course19 Minutes
- 2.2Getting started with code: Feel of code12 Minutes
- 2.3Foundations, data types and representing data21 Minutes
- 2.4Practical note: one-hot vector encoding5 Minutes
- 2.5Exploring data types in code12 Minutes
- 2.6Central tendency mean, median, mode20 Minutes
- Measures of Spread2
- Applications and Rules for Probability6
- 4.1Intro to uncertainty, probability intuition12 Minutes
- 4.2simulating coin flips for probability17 Minutes
- 4.3Dispersion exploration through code22 Minutes
- 4.4Applying conditional probability – Bayes rule10 Minutes
- 4.5Application of Bayes rule in real world – Spam detection8 Minutes
- 4.6Spam detection – implementation issues10 Minutes
- Counting1
- Random Variables - Rationale and Applications7
- 6.1Quantifying Events – random variables10 Minutes
- 6.2Two random variables – joint probabilities14 Minutes
- 6.3Distributions – rationale and importance18 Minutes
- 6.4Discrete distributions through code5 Minutes
- 6.5Continuous distributions – probability densities20 Minutes
- 6.6Continuous distributions code5 Minutes
- 6.7Case study – sleep analysis, structure and code18 Minutes
- Visualization In Intuition Building2
- Applications to the Real World5
- 8.1Expected values – decision making through probabilities6 Minutes
- 8.2Entropy – The most important application of expected values19 Minutes
- 8.3Applying entropy – coding decision trees for machine learning27 Minutes
- 8.4Foundations of Bayesian inference12 Minutes
- 8.5Bayesian inference code through PyMC36 Minutes
I hold PhD in Computer Sciences and a PostDoc from the Max Planck Institute for Software Systems. I have been programming since early 2000 and have worked with many different languages, tools and platforms. I have an extensive research experience with many state-of-the-art models to my name. My research in Android security has led to some major shifts in the Android permission model.
I love teaching and the most important reason I upload online is to make sure people can find my content. If you have any problem with finances and you want to take my courses, please visit my site (link on the left). I am more than willing to give out coupons that will make the course more affordable for you.
You can see all the different areas I've worked with on my site as well as on my github page.
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