Statistics & Probability for Data Science - 25+ Projects

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Manifold AI Learning ®

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1. Section 1 Foundation of Statistics

1. Introduction to Statistics.mp4

17:20

2. Types of Statistical Analysis - Descriptive Statistics.mp4

11:56

3. Types of Statistical Analysis - Inferential Statistics.mp4

16:31

4. How Statistics and Machine Learning are Related.mp4

10:15

5. Understanding the Types of Data.mp4

17:51

6. Sampling Techniques.mp4

24:02

7. Descriptive Statistics - Measure of Central Tendency.mp4

13:46

8. Descriptive Statistics - Measures of Dispersion - Range & Interquartile Range.mp4

13:54

9. Descriptive Statistics - Measures of Dispersion - Variance & Standard Deviation.mp4

08:04

10. Hands On - Exercise with Python.mp4

12:42

11. Descriptive Statistics - Measures of Shape.mp4

17:53

12. Descriptive Statistics - Measures of Position.mp4

08:24

13. Descriptive Statistics - Standard Scores.mp4

10:27

14. Descriptive Statistics - Hands On.mp4

10:42

15. Problem Statement - Wine Reviews Data Set Analysis.mp4

02:33

16. Solution for Project 1.mp4

16:01

17. Project 2 - Customer Income Data Analysis.mp4

02:55

18. Solution for Project 2.mp4

10:13

19. Project 3 - US Arrests Dataset.mp4

02:19

20. Solution for Project 3 - US Arrests Dataset.mp4

14:03

21. Project 4 - BigMart Sales data analysis.mp4

03:10

22. Solution for Big Mart Data Analysis.mp4

13:24

2. Section 2 Exploratory Data Analysis

1. Introduction to Exploratory Data Analysis.mp4

09:55

2. Types of Data Analysis.mp4

04:25

3. Univariate Non Graphical EDA & Outlier Analysis.mp4

14:11

4. Univariate Graphical EDA & Hands On.mp4

25:00

5. Multivariate Non Graphical EDA.mp4

17:17

6. Multi variate Graphical EDA.mp4

17:39

7. Steps in EDA.mp4

08:21

8. Summary of Graphical EDA Techniques.mp4

03:11

9. Hands On EDA on Titanic Data Set.mp4

01:08:46

10. Project 5 - Crimes in Boston City.mp4

03:08

11. Project 5 - Solution.mp4

15:17

12. Project 6 - PUBG Game Analysis.mp4

03:35

13. Project 6 - PUBG Game Analysis - Solution.mp4

38:55

14. Project 7 - FIFA Game Analysis.mp4

02:24

15. Project 7 - Solution.mp4

11:43

16. Project 8 - Covid19 Data Analysis.mp4

02:29

17. Project 8 Solution.mp4

18:29

3. Section 3 - Probability

1. Introduction to Probability.mp4

11:30

2. Key Terminology of Probability.mp4

09:53

3. Rules of Probability.mp4

06:08

4. Marginal Probability , Joint Probability.mp4

18:13

5. Disjoint Events and Non Disjoint events.mp4

06:08

6. Independent and Dependent events.mp4

05:42

7. Product Rule of Dependent & Independent Events.mp4

11:23

8. Task with Manifold Bank and compute probability.mp4

23:18

9. Bayes Theorem.mp4

05:12

10. Bayes Theorem in Data Science.mp4

02:29

11. Hands On Bayes Algorithm in Python.mp4

17:38

12. Random Variables.mp4

07:18

13. Various Distribution functions.mp4

13:27

14. Hands ON Generate the Discrete & Continuous Random numbers.mp4

09:30

15. Central Limit Theorem and Hands On.mp4

07:31

16. Applications of Probability Distributions.mp4

03:50

17. Hands On Transform the data to get Normal Distribution curve.mp4

24:56

18. Example Problems for Probability.mp4

09:23

19. Project 9 - Cars Dataset & Solution.mp4

09:33

20. Hands On - Bayes Theorem.mp4

07:04

21. Project 10 - Hands On - Normal Distribution & CDF.mp4

08:04

4. Section 4 - Inferential Statistics

1. Introduction to Inferential Statistics.mp4

08:01

2. Key Terminology of Inferential Statistics.mp4

03:10

3. Hands On - Population & Sample.mp4

07:07

4. Types of Statistical Inference.mp4

07:23

5. Confidence Interval - Margin of Error - Confidence Interval Estimation.mp4

07:09

6. Demo - Margin of Error and Confidence Interval.mp4

06:08

7. Hypothesis Testing & Steps of Hypothesis testing.mp4

06:46

8. ZTest and Example Problem.mp4

03:57

9. ZTest Solution Hands On.mp4

05:27

10. 1 Sample t-test.mp4

03:43

11. 1 sample t-test Hands On.mp4

04:17

12. 2 Sample t-test.mp4

02:28

13. 2 sample t-test Hands On.mp4

03:51

14. Paired Sample t-test.mp4

01:42

15. Hands On - Paired Sample t-test.mp4

04:51

16. Chi-Square Goodness of Fit.mp4

02:35

17. Hands On - Chi Square test.mp4

02:53

18. Anova.mp4

01:41

19. Hands On - Anova.mp4

04:09

20. Project 11 - Inferential Statistics - cars.mp4

02:15

21. Project 11 - Solution.mp4

07:24

22. Project 12 - Blood Pressure health dataset.mp4

01:50

23. Project 12 - Solution.mp4

04:12

24. Project 13 - Students admissions dataset.mp4

01:51

25. Project 13 - Solution.mp4

03:19

5. Section 5 - Linear Regression

1. Introduction to Regression , What , Why and Types of Problem we can solve.mp4

10:08

2. Assumptions of Linear Regression.mp4

03:24

3. Intuition of Linear Regression.mp4

07:25

4. Linear Regression with Normal Equation.mp4

10:48

5. Apply Linear Regression using Sklearn - Hands On.mp4

11:57

6. Checking Assumption of Linear Regression - Hands On.mp4

15:36

7. How Good is your fit .mp4

03:45

8. How Minimisation of Error is performed - Gradient Descent.mp4

18:01

9. Gradient Descent Hands On Part 1.mp4

19:33

10. Gradient Descent Hands On Part 2.mp4

12:49

11. Project 14 - Hands On - Implementation of Linear Regression using StatsModels.mp4

01:56

12. Project 14 - Solution.mp4

16:49

13. Project 15 - Salary Prediction Problem Statement.mp4

01:52

14. Project 15 - Solution.mp4

09:57

15. Project 16 - House Price Prediction Dataset.mp4

02:07

16. Project 16 - Solution.mp4

07:03

17. Project 17 - Medical Cost Prediction.mp4

02:12

18. Project 17 Solution.mp4

09:45

19. Project 18 - Company Profit prediction.mp4

02:32

20. Project 18 - Solution.mp4

08:09

6. Section 6 - Logistic Regression

1. Introduction to Logistic Regression.mp4

04:15

2. Hands On - Logistic Regression Plot.mp4

10:00

3. Assumptions of Logistic Regression.mp4

02:18

4. Logistic Regression from Scratch.mp4

15:22

5. Project 19 - Diabetes Prediction.mp4

01:50

6. Project 19 - Solution.mp4

10:33

7. Project 20 - Heart Disease Prediction.mp4

01:58

8. Project 20 - Solution.mp4

08:05

9. Project 21 - Titanic Survival Dataset.mp4

01:52

10. Project 21 - Solution.mp4

08:18

11. Project 22 - Nursery Student Dataset.mp4

01:44

12. Project 22 - Solution.mp4

04:05

7. Section 7 - Miscellaneous Stats Concepts in Machine Learning Areas

1. Resampling Technique.mp4

05:43

2. Cross validation Techniques Hands On.mp4

23:14

3. Project 23 - Flight Price Prediction.mp4

02:07

4. Project 23 Solution.mp4

17:59

5. Project 24 - Concrete Compressive Strength.mp4

02:33

6. Project 24 - Solution.mp4

11:16

7. Project 25 - US Baseball Salary prediction.mp4

01:23

8. Project 25 - Solution.mp4

09:38

Description

Gain control over your data with Math & Stats foundation required for Data Science, Machine Learning & Deep learning

What You'll Learn?

Learn Underlying Mathematics to build an intuitive understanding & relating it to Machine Learning and Data Science
Hands-On Code Implementation with Python for each mathematical topic to deepen the knowledge
Master the Advanced level in an Interactive learning approach to Strengthen your knowledge on Difficult & Important Topics
Understand the Importance of Probability & Distributions, and choose the right function for your data.

Who is this for?

Anyone who wants to understand the fundamentals underlying the abstractions of ML Algorithms, and expand the capabilities

A software developer who wants to develop the firm foundation for the deployment of Machine learning Algorithms into Production Systems

A Data Scientist who wants to reinforce the understanding of the Subjects at the core of the professional discipline

A Data Analyst or A.I enthusiast who wants to become a data scientist or ML Engineer and are keen to deeply understand the field that you are entering from Level Zero.

More details

Description
The Growing availability of data has made way for Data Science and Machine Learning to become in-demand professions. We define Statistics for Data Science - Predictive Analytics as exposure to Statistics which is essential for anyone seeking a career in Data Science and Machine learning. In this course, you will get the required college math , statistics and its practical implementation fromÂ Data Analytics which are necessary to better understand what goes in the black box libraries(sklearn) that you would encounter in the Data Science Journey.

With this course, as a learner, you will be exposed to various Statistics and Machine Learning topics that will apply to real-world problems.

The Ultimate goal of taking this structured approach is to integrate everything we learn and demonstrate practical insights in using Machine learning and Statistical Libraries beyond a black-box understanding.

Why Learn from Us ??
I am a Lead Data Scientist at Manifold AI Learning, an e-learning company which is into creation of e-learning courses in the field of Data Science, Machine Learning & Deep learning. Having founded in the year of 2015, till now our YouTube Channel has more than 75k views around the globe, and 17k+ happy learners on Udemy having each of the course being the best in its specific topic. Apart from publishing the courses standalone , we have created some of the Top class products for Well-known brands in e-learning domain.

As a Lead Data Scientist at Manifold AI Learning, apart from creating the e-learning content, I also provide the Consulting Services enabling the companies to perform End to End Implementation of Data Science projects from initial Client interaction, Experimentation of Models, Operationalisation of Machine Learning Models in Production Environment, followed by Maintenance of Machine Learning Models. I have worked with more than 15 companies and helping them achieve more than 2M$ in collective revenue over the period of my Involvement with Clients.

As a person who works closely with Business and the key challenges in its implementation, combined with my ability to create Interactive Courses, I would be a right fit to teach the aspiring learners of Data Science and Machine Learning on this important topic of Mathematics and Stats for Data Science.
Who this course is for:
Anyone who wants to understand the fundamentals underlying the abstractions of ML Algorithms, and expand the capabilities
A software developer who wants to develop the firm foundation for the deployment of Machine learning Algorithms into Production Systems
A Data Scientist who wants to reinforce the understanding of the Subjects at the core of the professional discipline
A Data Analyst or A.I enthusiast who wants to become a data scientist or ML Engineer and are keen to deeply understand the field that you are entering from Level Zero.

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Manifold AI Learning ® is an online Academy with the goal to empower the students with the knowledge and skills that can be directly applied to solving the Real world problems in Data Science, Machine Learning and Artificial intelligence.Checkout our instructor profile for the complete list of courses.All the best for your Learning.- Team ManifoldAILearning ®"Learn the Future"

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