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Lesson Posted on 19 Jul IT Courses/Data Science

What are Kalman filters? Why they are popular in AI?

Harani Mathuku

I have total 7 years of IT experience with extensive knowledge on Data science, Machine learning, Deep...

Imagine we are making a self-driving car and we are trying to localize its position in an environment. The sensors of the vehicle can detect cars, pedestrians, and cyclists. Knowing the location of these objects can help the car make judgements, preventing collisions. But other than knowing the location... read more

Imagine we are making a self-driving car and we are trying to localize its position in an environment. The sensors of the vehicle can detect cars, pedestrians, and cyclists. Knowing the location of these objects can help the car make judgements, preventing collisions. But other than knowing the location of the objects, the car needs to predict their future locations so that it can plan what to do ahead of time. For example, if it were to detect a child running towards the road, it should expect the child not to stop. The Kalman filter can help with this problem, as it is used to assist in tracking and estimation of the state of a system.

The car has sensors that determine the position of objects, as well as a model that predicts their future positions. In the real world, predictive models and sensors aren’t perfect. There is always some uncertainty. For example, the weather can affect the incoming sensory data, so the car can’t completely trust the information. With Kalman filters, we can mitigate the uncertainty by combining the information we do have with a distribution that we feel more confident.

 

Usage of the Kalman filter acknowledges that some error and noise is implicit within both the prediction and measurement of the variables we’re interested in tracking.

The filter does not assume all errors are Gaussian, but as cited from the Wikipedia description, the “filter yields the exact conditional probability estimate in the special case that all errors are Gaussian.” Recall, that the Gaussian is characterized by two parameters: the mean (mu) and the width of Gaussian, namely the variance (sigma squared).

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Lesson Posted on 19 Jul IT Courses/Data Science Artificial Intelligence

What are Kalman filters? Why they are popular in AI?

Tasneem

I am a teacher in the oxford college of dental and my specialization is orthodontistry. i have a degree...

Imagine we are making a self-driving car and we are trying to localize its position in an environment. The sensors of the vehicle can detect cars, pedestrians, and cyclists. Knowing the location of these objects can help the car make judgements, preventing collisions. But other than knowing the location... read more

Imagine we are making a self-driving car and we are trying to localize its position in an environment. The sensors of the vehicle can detect cars, pedestrians, and cyclists. Knowing the location of these objects can help the car make judgements, preventing collisions. But other than knowing the location of the objects, the car needs to predict their future locations so that it can plan what to do ahead of time. For example, if it were to detect a child running towards the road, it should expect the child not to stop. The Kalman filter can help with this problem, as it is used to assist in tracking and estimation of the state of a system.

The car has sensors that determine the position of objects, as well as a model that predicts their future positions. In the real world, predictive models and sensors aren’t perfect. There is always some uncertainty. For example, the weather can affect the incoming sensory data, so the car can’t completely trust the information. With Kalman filters, we can mitigate the uncertainty by combining the information we do have with a distribution that we feel more confident.

 

Usage of the Kalman filter acknowledges that some error and noise is implicit within both the prediction and measurement of the variables we’re interested in tracking.

The filter does not assume all errors are Gaussian, but as cited from the Wikipedia description, the “filter yields the exact conditional probability estimate in the special case that all errors are Gaussian.” Recall, that the Gaussian is characterized by two parameters: the mean (mu) and the width of Gaussian, namely the variance (sigma squared).

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Answered on 11 Jul IT Courses/Data Science

Paritosh Sharma

I would suggest Data Science with R. Since you are in HR domain it would be easier for you to understand the programming.
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Answered on 12/12/2018 IT Courses/Data Science

Aritra Kumar Sinha

Hi, First of all thanks for the question. Data Science as a subject has multiple layers. A great way to get started would be to brush up basic statistical concepts. Fundamental concepts of probability, distributions and Hypothesis Testing can get you started working on certain Data Science problems.... read more

Hi,

First of all thanks for the question. Data Science as a subject has multiple layers. A great way to get started would be to brush up basic statistical concepts. Fundamental concepts of probability, distributions and Hypothesis Testing can get you started working on certain Data Science  problems. For example, if a marketing campaign has taken place during Diwali and some customers are given some offers , what is the best way to measure the impact of the campaign? How can we say that the impact was statistically significant ? 

Problems of supervised and unsupervised learning however requires deeper understanding. Multiple online available sources can help you get clarity on concepts. The key thing is to know what topics to search for. Here is a starting  list of topics you would want to research on with regards to unsupervised learning

   Principal Component and Factor         Analysis

   Cluster Analysis

    Conjoint Analysis

    Multi Dimensional Scaling

Topics on Supervised Learning could be and not limited to 

   Regression (OLS and GLM) 

   Non Parametric methods such as

      Decision Trees

      Random Forest

       SVM

       Boosting Techniques

Once you get the gist of the concepts on these topics , it will be much easier for you to grasp the discussions in any training program if you join one. In terms of programming Python is agreat platform .A few years ago, SAS was almost always important but things have changed now. SkLearn offers great set of packages in Python to play with. I'd encourage you to try and apply algorithmic approaches to create your own functions in Python. In conclusion, in my experience Data Science has been a journey and not a cluster of skills. Over time it will become a part of your thought process and will guide you every step of the way. Keep learning and keep practicing. 

If you need to discuss anything further - reach out to me.

Aritra

 

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Answered on 29 Jan IT Courses/Data Science

Ekta Saraogi

Freelancer Data Scientist and Trainer, Maths and Computer Science Tutor

Data Science is a vast field. First of all you should learn statistics which is very important in Data Science field. Then you need to learn about basic Data Analytics and concepts. Languauges like SAS, R , Python can be chosen and learnt if you have the right concepts. All of these are based on the... read more

Data Science is a vast field. First of all you should learn statistics which is very important in Data Science field. Then you need to learn about basic Data Analytics and concepts. Languauges like SAS, R , Python can be chosen and learnt if you have the right concepts. All of these are based on the similar Data Science algorithms and learning a language is not difficult if you have your concepts very strong and domain knowledge. If you want to learn Data Science in a structured manner, please contact me as I have a very comprehensive course which can also be modified to suit individual needs and requirements.

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Answered on 10/11/2018 IT Courses/Data Science

How can I land a job in data science and analytics in India?

Shibasis Namdev

IT Professional and trainer with 5 years of experiences in Tableau

Using only 2 steps.. 1.Learn about Machine learning using pyghon/ R and do some homework (Project). 2. Apply by various job portal. Most important thing is "Learn everyday" Best of Luck
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Answered on 10/11/2018 IT Courses/Data Science

Do I need a Masters/PhD to become a data scientist?

Shibasis Namdev

IT Professional and trainer with 5 years of experiences in Tableau

Not required, basic understanding on Computer prigramming and Statistics and common sense with graduation degree (marks also not imp). Industries need talent to process the data to get valuable output not highly qualified people without skill. Best of luck
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Answered on 03/11/2018 IT Courses/Data Science

Which colleges in india provide data science courses?

Alok Jain

SHRIRAM COLLEGE, MUZAFFARNAGAR
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Answered on 10/11/2018 IT Courses/Data Science

What is the biggest no-no in data science?

Shibasis Namdev

IT Professional and trainer with 5 years of experiences in Tableau

Stagnation and ego... Better to update yourself everyday with updated technology and be humble.
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Answered on 01/08/2018 IT Courses/Data Science IT Courses/Hadoop IT Courses/SAP

Sabitri Panda

SAP HCM/ SAP Successfactor Instructor

Hi Both have different uniquness with importance value. you will get a good prospectives on SAP for career growth.
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