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Your Way to Machine Learning With Python

Steps to be Pro Machine Learning Coder with Python

What is Machine Learning?

It’s one of the Artificial Intelligence approaches. The ability of a computer to learn by being exposed to tons of data. Computers will learn, grow, and enhance their abilities. There are many huge companies use Machine Learning to improve their service quality such as:

  • Google in self-driving cars
  • Cybersecurity Companies
  • Facebook
  • Amazon

According to simplilearn, Machine Learning process flow

Machine Learning computers learn like a human, it keeps training to reach the best possible results. Big data has become so much advanced. With Machine Learning, it is easier now to analyze and extract a huge amount of data.

Then, how to master Machine Learning?

First, Basic Python Syntax

You need to have a sound knowledge of Python. Machine Learning is not as hard as it sounds. However, knowing Python basics save you a lot of time and help you to hit your goal faster.

Second, Python Packages for Scientists

Well, not exactly for scientists only, but they are used by scientists, engineers or companies. These packages are important when we implement Machine Learning, it makes things simpler, such as

  • pandas: Used for data-frames and data analysis.
  • numpy: Used for dimensional arrays.
  • scikit-learn: Data mining and data analysis for Machine Learning.
  • matplotlib: 2D plotting figures library.

       Recommended book:

Third, Machine Learning Topics and Areas

There are many useful Machine Learning algorithms used in different areas and applications such as.   

1- K-means Clustering: It’s unsupervised learning type, which is used when dealing with uncategorized data to find groups in the data. Used in Marketing Strategies, Crisis Prevention Techniques (e.g. earthquake).

2- Decision TreesIt’s used in classifications and regression to predict future values by learning the rules extracted from the data features

3- Linear RegressionIt’s a statistical method that helps to find the relationships between 2 quantitative variables. Used in Finance and Economics.

4- Logistic RegressionIt’s a statistical method for analyzing data of one or more independent variables. It helps to find the best model to explain the relationship between a set of independent variables and dichotomous characteristic of interest. Used in Medicine, Financial Forecasting, Cost Predictions.

5- Support Vector Machines (SVM) - (Advanced): It’s supervised learning method used for regression and classification. Used in Bioinformatics, Face Detection, Image Classification, Texts Categorization.

Finally, The best thing to do to master any skill is to practice, practice and practice. Doing projects and exercises should help you enhance your coding abilities

Happy Coding...


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