Machine Learning & AI with Python | Mathematics & Statistics

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EDUCBA Bridging the Gap

7:59:38

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1. Introduction

1. Introduction to Machine Learning with Python.mp4

03:28

2. Importing

1. Machine Learning Introduction.mp4

04:44

2. Analytics in Machine Learning.mp4

09:33

3. Big Data Machine Learning.mp4

07:57

4. Emerging Trends Machine Learning.mp4

08:45

5. Data Mining.mp4

08:21

6. Data Mining Continues.mp4

06:58

7. Supervised and Unsupervised.mp4

07:52

3. Basics of Statistics Sampling

1. Sampling Method in Machine Learning.mp4

07:34

2. Technical Terminology.mp4

11:25

3. Error of Observation and Non Observation.mp4

07:05

4. Systematic Sampling.mp4

08:26

5. Cluster Sampling.mp4

10:52

4. Basics of Statistics Data types and Visualization

1. Statistics Data Types.mp4

05:10

2. Qualitative Data and Visualization.mp4

07:52

5. Basics of Statistics Probability

1. Machine Learning.mp4

08:25

2. Relative Frequency Probability.mp4

09:13

3. Joint Probability.mp4

10:26

4. Conditional Probability.mp4

08:34

5. Concept of Independence.mp4

06:32

6. Total Probability.mp4

10:19

6. Basics of Statistics Random Variables

1. Random Variable.mp4

08:58

2. Probability Distribution.mp4

11:17

3. Cumulative Probability Distribution.mp4

09:30

7. Basics of Statistics Distributions

1. Bernoulli Distribution.mp4

08:56

2. Gaussian Distribution.mp4

08:18

3. Geometric Distribution.mp4

08:03

4. Continuous and Normal Distribution.mp4

10:11

8. Matrix Algebra

1. Mathematical Expression and Computation.mp4

08:56

2. Transpose of Matrix.mp4

08:59

3. Properties of Matrix.mp4

11:35

4. Determinants.mp4

09:53

9. Hypothesis Testing

1. Error Types.mp4

09:02

2. Critical Value Approach.mp4

08:45

3. Right and Left Sided Critical Approach.mp4

09:57

4. P-Value Approach.mp4

10:44

5. P-Value Approach Continues.mp4

09:16

6. Hypothesis Testing.mp4

10:45

7. Left Tail Test.mp4

05:30

8. Two Tail Test.mp4

09:50

9. Confidence Interval.mp4

08:49

10. Example of Confidence Interval.mp4

11:09

10. Hypothesis Tests-Types

1. Normal and Non Normal Distribution.mp4

09:34

2. Normality Test.mp4

09:30

3. Normality Test Continues.mp4

10:12

4. Determining the Transformation.mp4

06:14

5. T-Test.mp4

11:17

6. T-Test Continue.mp4

08:29

7. More on T-Test.mp4

09:06

8. Test of Independence.mp4

10:43

9. Example of Test of Independence.mp4

09:39

10. Goodness of Fit Test.mp4

06:42

11. Example of Goodness of Fit Test.mp4

07:10

11. Regression

1. Co-Variance.mp4

05:28

2. Co-Variance Continues.mp4

07:40

Description

Learn the core mathematical concepts, Probability, Statistics, Data Science, Data Analytics, Machine and Deep Learning

What You'll Learn?

Understand and implement Regression, Classification, and Clustering algorithms
Learn Linear Algebra, Calculus for Machine and Deep Learning
Build Mathematical intuition especially Calculus required for Deep learning, Data Science and Machine Learning
Refresh the mathematical concepts for AI and Machine Learning

Who is this for?

Beginners who want to learn Data Science and Machine Learning

Practitioners and experts who want to get a refresher of the maths for machine learning

What You Need to Know?

Some basic concepts of linear algebra and calculus

Familiarity with secondary school-level mathematics will make the class easier to follow along with.

More details

Description
The future for AI is extremely promising and it isnâ€™t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isnâ€™t easy and requires extensive programming and mathematical knowledge. Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math. Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. However, understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increase the impact you can make over the course of your career. This is a highly comprehensive Mathematics, Statistics, and Probability course, you learn everything from Set theory, Combinatorics, Probability, statistics, and linear algebra to Calculus with tons of challenges and solutions for Business Analytics, Data Science, Data Analytics, and Machine Learning. Mathematics, Probability & Statistics are the bedrock of modern science such as machine learning, predictive risk management, inferential statistics, and business decisions. In this course, we will cover right from the foundations of Algebraic Equations, Linear Algebra, Calculus including Gradient using Single and Double order derivatives, Vectors, Matrices, Probability and much more.
Mathematics form the basis of almost all the Machine Learning algorithms. Without maths, there is no Machine Learning. Machine Learning uses mathematical implementation of the algorithms and without understanding the math behind it is like driving a car without knowing what kind of engine powers it.
You may have studied all these math topics during school or universities and may want to freshen it up. However, many of these topics, you may have studied in a different context without understanding why you were learning them. They may not have been taught intuitively or though you may know majority of the topics, you can not correlate them with Machine Learning.
This course of Math For Machine Learning, aims to bridge that gap. We will get you upto speed in the mathematics required for Machine Learning and Data Science. We will go through all the relevant concepts in great detail, derive various formulas and equations intuitively.
Who this course is for:
Beginners who want to learn Data Science and Machine Learning
Practitioners and experts who want to get a refresher of the maths for machine learning

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