Machine Learning with Python_1

Machine Learning with Python, available at $19.99, has an average rating of 2.2, with 26 lectures, based on 5 reviews, and has 527 subscribers.

You will learn about Machine Learning using Python This course is ideal for individuals who are Beginner Data Scientist or Analyst interested in Python programming It is particularly useful for Beginner Data Scientist or Analyst interested in Python programming.

Enroll now: Machine Learning with Python

Summary

Title: Machine Learning with Python

Price: $19.99

Average Rating: 2.2

Number of Lectures: 26

Number of Published Lectures: 26

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Machine Learning using Python

Who Should Attend

Beginner Data Scientist or Analyst interested in Python programming

Target Audiences

Beginner Data Scientist or Analyst interested in Python programming

Why learn Data Analysis and Data Science?

According to SAS, the five reasons are

1. Gain problem solving skills

The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.

2. High demand

Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.

3. Analytics is everywhere

Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It’s a hugely exciting time to start a career in analytics.

4. It’s only becoming more important

With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.

5. A range of related skills

The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths.  Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.

The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.

This is the bite-size course to learn Python Programming for Machine Learning and Statistical Learning. In CRISP-DM data mining process, machine learning is at the modeling and evaluation stage. 

You will need to know some Python programming, and you can learn Python programming from my “Create Your Calculator: Learn Python Programming Basics Fast” course.  You will learn Python Programming for machine learning and you will be able to train your own prediction models with Naive Bayes, decision tree, knn, neural network, and linear regression, and evaluate your models very soon after learning the course.

I have created Applied statistics using Python for the data understanding stage and advanced data visualizations for the data understanding stage and including some data processing for the data preparation stage.

You can look into the following courses to get SVBook Certified Data Miner using Python

SVBook Certified Data Miner using Python is given to people who have completed the following courses:

– Create Your Calculator: Learn Python Programming Basics Fast (Python Basics)

– Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation)

– Advanced Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation)

– Machine Learning with Python (Modeling and Evaluation)

and passed a 50 questions Exam. The four courses are created to help learners understand about Python programming basics, then applied statistics (descriptive, inferential, regression analysis) and data visualizations (bar chart, pie chart, boxplot, scatterplot matrix, advanced visualizations with seaborn, and Plotly interactive charts ) with data processing basics to understand more about the the data understanding and data preparation stage of IBM CRISP-DM model. The learner will then learn about machine learning and confusion matrix, which are the modeling and evaluation stages of the IBM CRISP-DM model. Learners will be able to do data mining projects after learning the courses.

Content

1. Getting Started

2. Getting Started 2

3. Getting Started 3

4. Getting Started 4

5. Data Mining Process

6. Download Data set

7. Read Data set

8. Simple Linear Regression

9. Build Linear Regression Model: Train and Test set

10. Build and Predict Linear Regression Models

11. KMeans Clustering

12. KMeans Clustering in Python

13. Agglomeration Clustering

14. Agglomeration Clustering in Python

15. Decision Tree ID3 Algorithm

16. Decision Tree in Python

17. KNN Classification

18. KNN in Python

19. Naive Bayes Classification

20. Naive Bayes in Python

21. Neural Network Classification

22. Neural Network in Python

23. What Algorithm to Use?

24. Model Evaluation

25. Model Evaluation using Python for Classification

26. Model Evaluation using Python for Regression

Course Curriculum

Chapter 1: Session

Lecture 1: Getting Started 1

Lecture 2: Getting Started 2

Lecture 3: Getting Started 3

Lecture 4: Getting Started 4

Lecture 5: Data Mining Process

Lecture 6: Download Dataset

Lecture 7: Read CSV

Lecture 8: Simple Linear Regression

Lecture 9: Simple Linear Regression using Python – Train and Test set

Lecture 10: Simple Linear Regression using Python – train and predict

Lecture 11: KMeans Clustering

Lecture 12: KMeans Clustering in Python

Lecture 13: Agglomeration CLustering

Lecture 14: Agglomeration CLustering in Python

Lecture 15: Decision Tree Algorithm: ID3

Lecture 16: Decision Tree in Python

Lecture 17: KNN Classification

Lecture 18: KNN Classification in Python

Lecture 19: Naive Bayes ALgorithm

Lecture 20: Naive Bayes in Python

Lecture 21: Neural Network

Lecture 22: Neural Network in Python

Lecture 23: What Algorithm to use?

Lecture 24: Model Evaluation

Lecture 25: Model Evaluation for Classification in Python

Lecture 26: Model Evaluation for Regression in Python

Instructors

Goh Ming Hui
Offer affordable data science courses.

Rating Distribution

1 stars: 3 votes

2 stars: 0 votes

3 stars: 1 votes

4 stars: 1 votes

5 stars: 0 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!