Learn how to use NumPy, Pandas, Matplotlib, Scikit-Learn, Tensorflow with a complete statistics course as appendix
If you seek to learn how to create machine learning models and use them in data mining process, this course is for you. You will understand in this course what is data mining process and how to implement machine learning algorithms in data mining. Moreover, you will learn in details how deep learning does work and how to build a deep learning model to solve a business problem. In the beginning of the course, you will understand the basic concepts of data mining and learn about the business fields where data mining is implemented. After that you will learn how to create machine learning models in Python using several data science libraries developed especially for this purpose. NumPy, Pandas, and Matplotlib are some examples of these models that you will learn how to import and use to create machine learning algorithms in Python. You will learn typing codes in Python from scratch without the need to have a pervious knowledge in coding. You will be familiar with the essential code needed to build machine learning models. This course is designed to provide you with the knowledge you need in a simple and straightforward way to smooth the learning process. You will build your knowledge step by step until you become familiar with the most used Machine Learning algorithms.
What you’ll learn
- Learn everything about Data Mining and its applications.
- Understand Machine Learning and its connection with Data Mining.
- Learn all Machine Learning algorithms, their types, and their usage in business.
- Learn how to implement Machine Learning algorithms in different business scenarios.
- Learn how to install and use Python programming language to create machine learning algorithms in a simple way.
- Learn how to import your data sets into Python and make required cleaning before creating the algorithms.
- Learn how to interpret the results of each algorithms and compare them with each other to choose the optimum one.
- Learn how to create graphs in Pythons, such as scattered and regression graphs and use them in your analyses.
Course Content
- Introduction –> 11 lectures • 25min.
- Setup Programming Environment –> 8 lectures • 37min.
- Supervised Learning Algorithms –> 50 lectures • 3hr 29min.
- Unsupervised Learning Algorithms –> 15 lectures • 1hr 14min.
- Deep Learning –> 10 lectures • 44min.
- Appendix: Statistics Overview –> 41 lectures • 2hr 9min.
Requirements
If you seek to learn how to create machine learning models and use them in data mining process, this course is for you. You will understand in this course what is data mining process and how to implement machine learning algorithms in data mining. Moreover, you will learn in details how deep learning does work and how to build a deep learning model to solve a business problem. In the beginning of the course, you will understand the basic concepts of data mining and learn about the business fields where data mining is implemented. After that you will learn how to create machine learning models in Python using several data science libraries developed especially for this purpose. NumPy, Pandas, and Matplotlib are some examples of these models that you will learn how to import and use to create machine learning algorithms in Python. You will learn typing codes in Python from scratch without the need to have a pervious knowledge in coding. You will be familiar with the essential code needed to build machine learning models. This course is designed to provide you with the knowledge you need in a simple and straightforward way to smooth the learning process. You will build your knowledge step by step until you become familiar with the most used Machine Learning algorithms.