Data Science & Machine Learning: Naive Bayes in Python

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Lazy Programmer Inc.,Lazy Programmer Team

5:00:43

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1. Welcome

1. Introduction and Outline.mp4

04:36

2.1 Bonus Notebooks.html

2.2 Github Link.html

2. Where to get the Code.mp4

02:10

3. Are You Beginner, Intermediate, or Advanced All are OK!.mp4

05:01

4. How to Succeed in this Course.mp4

08:42

2. Naive Bayes Concepts (Beginner)

1. Concepts Section Introduction.mp4

01:48

2. Classification Review.mp4

14:59

3. Bayes' Rule Review.mp4

09:13

4. Naive Bayes Intuition.mp4

17:29

5. Concepts Section Summary.mp4

03:01

6. Suggestion Box.mp4

03:10

3. Naive Bayes Applications (Beginner-Intermediate)

1. Applications Section Introduction.mp4

05:22

2. Strategy and Approach.mp4

01:49

3. Disease Prediction with Naive Bayes.mp4

07:08

4. Disease Prediction with Naive Bayes in Python (pt 1).mp4

12:41

5. Disease Prediction with Naive Bayes in Python (pt 2).mp4

10:48

6. Finance with Naive Bayes.mp4

05:24

7. Finance with Naive Bayes in Python (pt 1).mp4

15:28

8. Finance with Naive Bayes in Python (pt 2).mp4

08:04

9. Genomics with Naive Bayes.mp4

07:33

10. Genomics with Naive Bayes in Python.mp4

07:05

11. Image Classification with Naive Bayes.mp4

11:04

12. Image Classification with Naive Bayes in Python.mp4

11:25

13. Text Classification with Naive Bayes (pt 1).mp4

16:35

14. Text Classification with Naive Bayes (pt 2).mp4

03:01

15. Text Classification with Naive Bayes in Python.mp4

16:40

16. Applications Section Summary.mp4

01:39

17. Application Exercise.mp4

01:23

4. Naive Bayes In-Depth (Advanced)

1. Gaussian Naive Bayes Theory.mp4

32:32

2. Gaussian Naive Bayes in Python.mp4

22:57

3. Bernoulli Naive Bayes Theory.mp4

13:38

4. Multinomial Naive Bayes Theory.mp4

15:17

5. Exercises Test Your Might!.mp4

03:01

Description

Master a crucial artificial intelligence algorithm and skyrocket your Python programming skills

What You'll Learn?

Apply Naive Bayes to image classification (Computer Vision)
Apply Naive Bayes to text classification (NLP)
Apply Naive Bayes to Disease Prediction, Genomics, and Financial Analysis
Understand Naive Bayes concepts and algorithm
Implement multiple Naive Bayes models from scratch

Who is this for?

Beginner Python developers curious about data science and machine learning

Students and professionals interested in machine learning fundamentals

More details

Description
In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as:
computer vision
natural language processing
financial analysis
healthcare
genomics
Why should you take this course?Â Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.
This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You'll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You'll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.
In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!
Thank you for reading and I hope to see you soon!

Suggested Prerequisites:
Decent Python programming skill
Comfortable with data science libraries like Numpy and Matplotlib
For the advanced section, probability knowledge is required

WHATÂ ORDERÂ SHOULDÂ IÂ TAKEÂ YOURÂ COURSESÂ IN?
Check out the lecture "Machine Learning and AIÂ Prerequisite Roadmap" (available in the FAQ of any of my courses, including my free course)

UNIQUEÂ FEATURES
Every line of code explained in detail - email me any time if you disagree
Less than 24 hour response time on Q&AÂ on average
Not afraid of university-level math - get important details about algorithms that other courses leave out
Who this course is for:
Beginner Python developers curious about data science and machine learning
Students and professionals interested in machine learning fundamentals

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Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Lazy Programmer Team

Instructor's Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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