Machine Learning with Python

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