Statistics for Machine Learning | Statistics Class 10 | Statistics for Data Science | Full Course

A background in Statistics can prepare you for a wide range of job opportunities ranging from being a pure statistical researcher to a highly sought after data scientist. Keeping the importance of Statistics in mind, we have come up with this Statistics for Machine Learning Full Course.

This course will be taught by Great Learning faculty Dr. P. K. Viswanathan, who is ranked among the top 5 analytics professors in India and in Top 3 Most Prominent Analytics & Data Science Academicians in India. He has a rich and varied experience across academia, research, industry, training and consulting.

🏁 Topics Covered:
00:00:00 Agenda
00:02:42 Introduction to Statistical Methods for Decision Making
00:25:11 Probability and Bayes Theorem
02:02:23 Probability Distribution
03:08:33 Central Limit Theorem
03:24:55 Hypothesis Testing
04:58:30 Implementing the concepts with R

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[greatlearning]

These are the topics being covered in this video:
00:00:00 Agenda
00:02:42 Introduction to Statistical Methods for Decision Making
00:25:11 Probability and Bayes Theorem
02:02:23 Probability Distribution
03:08:33 Central Limit Theorem
03:24:55 Hypothesis Testing
04:58:30 Implementing the concepts with R

[akashbhullar]

Who are you people? How is this kind of Satkarm possible in Kalayuga!!
I mean I Thank You for these beautiful free lectures. God bless these teachers and YOU, For Uploading this. Subscribed.

[sambowwow]

I LOVE Indian professors!! when it comes to explaining concepts, no one is better than an Indian professor.

[greatlearning]

Guys, what else do you want to learn from us? Please do comment below:

[balamira]

Really one of the finest lectures without even spending a single penny! Great job team!

[hosamfikry]

Thank you for this set of lectures. I studied statistics in My Data Science Master's, but I wanted to revise the intuition and have a better understanding of the bigger picture. I finished one hour and I am feeling grateful already.
Thanks to the Indian culture of sharing knowledge and making education accessible.
With love,
An Egyptian potential data scientist

[greatlearning]

Also to access the datasets, projects, assessments, and codes, register on Great Learning Academy.
You can also enroll for any of our 80+ courses offering 1000+ hours of content for free.

[anilpillai]

I really love it when he approximates calculations and says " Mota Moti" 😂😂Similarly for canceling he says " Khatak" 😂😂...Its a south Indian accent but seems cute....The thing is...he enjoys doing sums, dats the basic essence of teaching n learning 😊Thanks Sir

[jyothsnaraajjj]

Amazing professor DR. Viswanathan sir.

[kunaljain]

The content is great.
Suggestion - You can divide the whole topic into modules of smaller length like Udacity does because people will start the video for sure but won't end the video. They will end up learning the rest elsewhere(Statquest).

[Nazeerul_Hazard]

Very energetic teacher. Teaches with passion

[chaykorra]

I have no words. All I can say is you are the finest set of educators in the entire globe. Kudos.

[pksddme]

My sincere Thanks for uploading this great lecture. I really enjoy the video and learn a lot. Pl convey my thanks to the great Professor and "Charan sprsh".

[somnathbanerjee]

He is very knowledgeable... admirable. He would be one of the best professors assigned with managers or directors in an organisation or in an institution.

[NewYorkJ]

if alpha is not given. Use alpha as 5% . In some experiments you can go upto to 10% mota moti you can take.

[anilpillai]

Please add more videos of Vishwanathan Sir, on left topics like Parametric and non-parametric tests

[NewYorkJ]

p-value is actual risk. Alpha is desired risk. when p vlaue is equal to or smaller than alpha - reject null hypothesis.

[akshaytelang]

thank you for this great lecture... can't believe it's not only free but ad-free

[NewYorkJ]

Step1: Formulate Null and Alternative Hypothesis Step 2: select appropriate statistics Step 3: compute z stats Step 4: plot the graph and mark rejection and acceptance. step 5. find out where statistics fall step 6. Make a decision. If it falls in the area then reject the hypothesis and approve alternative hypothesis.

[karthiky]

Excellent video. Suggest watching the video at 1.25 x speed