# 7 Steps to Mastering Machine Learning with Python

In any field of life, people always wants to be associated with the latest, be it gadgets, appliances or the technology. Applying this to the world of information technology, we see organizations investing strategically in Cloud Computing and IoT (Internet of Things). To get an edge over competitors, organizations have started investing into resource pools to enable the support that would be required at the back end of IoT and Cloud implementations and into Artificial Intelligence and Machine Learning.

Machine Learning is currently in its prime and young aspirant are looking to learn Machine Learning to ensure they are not caught lagging in the technology race. In addition, experienced ML professionals also need to stay up-to-date with the latest developments in Machine Learning to ensure smooth realignment & delivery of their projects. To help such professionals, in this article we present a an easy, 7-step process to get started with Machine Learning using the powerful Python programming language.

**Why Python?**

Python is the most popular programming language for machine learning professionals. This can be attributed to its rich feature-set that includes such features as:

- It is an object-oriented programming language and a very powerful scripting tool.
- Its syntax is very easy to understand.
- The interactive mode helps in testing short snippets of the code, even while developing the program.
- Python uses user-friendly data structures.
- Extensive standard library available for Python.

Let’s now look at the 7 step method to learn machine learning.

**Step 1: Basic Python Skills**

To use Python for Machine learning, we need to have some basic understanding of the programming language. Since Python is already in use for some time now for both machine learning & scientific calculations, it is not very difficult to start the learning process.

As we intend to use the language for both scientific computation and machine learning, it is advisable to install Anaconda. This version of python is supported by all popularly used operating systems like Linux, Windows and OSX. The version comes with the complete package for machine learning and includes numpy, matplotlib and scikit-learn.

In case you don’t have any prior knowledge or experience of programming, you can always start by referring to the following books:

- Python The Hard Way, by Zed A. Shaw
- Google Developers Python Course (highly recommended for visual learners)
- An Introduction to Python for Scientific Computing (from UCSB Engineering), by M. Scott Shell
- Learn X in Y Minutes (X = Python)

**Step 2: Foundation Machine Learning Skills**

This step is very important for anyone looking to get into data science as most of the tasks performed by data scientists involve some knowledge of Machine learning. Before getting into hands-on experimentation with various algorithms, it is very important for anyone, who wants to become data scientist, to gain enough theoretical knowledge. There are few very good online courses available, for anyone who is really interested to increase their knowledge in this field:

- Andrew Ng’s Machine Learning course on Coursera.
- Unofficial Andrew Ng course notes
- Tom Mitchell Machine Learning Lectures

These online courses will help the aspirants to have enough knowledge about Machine learning, before they venture into the unknown territories of Python.

**Step 3: Scientific Python Packages Overview**

You should now have a solid foundational understanding of the basics of Python programming used for Machine learning. At this stage, you can start exploring the various Python packages or libraries available that can be used along with Python to achieve better results.

The most popularly used Python libraries for machine learning applications are:

- numpy– This package is useful for its N-dimensional array objects
- pandas – This data analysis library includes structures like dataframes.
- matplotlib – This is a 2D plotting library which is generally used for creating publication quality figures.
- scikit-learn– This library is extensively used for data analysis and data mining tasks, needed for machine learning.

To learn about these packages in details, the following books can be referred:

- Scipy Lecture Notes, by Gaël Varoquaux, Emmanuelle Gouillart, and Olav Vahtras
- 10 Minutes to Pandas

**Step 4: Getting Started with Machine Learning in Python**

Till now we have learned the basics of Machine learning and Python, including the various libraries available. In this step, we can start implementing the machine learning algorithms using the library scikit-learn. An excellent interactive execution environment for Python algorithms is provided by iPython Notebook. These notebooks are available for the users online as well as offline. The following approach can be taken to gain more expertise:

- Gain more hands-on experience in scikit-learn, by working on sample projects, with a well-known data set.
- Strategize execution of various models in scikit-learn.
- Comparing various models.

This approach will prepare you for the next level.

**Step 5: Machine Learning Topics with Python**

Once you are well-versed with scikit-learn, we can proceed to the advanced levels by exploring various popular machine learning algorithms. Few of these algorithms are listed below:

- k-means Clustering: This clustering is used by unsupervised learning algorithms. In this method, n number of observations are partitioned into k clusters.
- Linear Regression: This is a linear approach for establishing relationship between X & Y, where X denotes one or more independent variables and Y denotes dependent variables.
- Logistic Regression: In this regression model, the dependent variable is generally categorical. There are 2 different models available for Logistic regression – binary logistic regression & multinomial logistic regression. The binary logistic regression deals with scenarios where the output can be only 2 values, either “0” or “1”. This represents outcomes like pass/fail, alive/dead or win/lose. In case of more than 2 possible outcomes, the multinomial logistic regression comes into play.

**Step 6: Advanced Machine Learning Topics with Python**

Now that we are well versed with scikit learning, time for us to explore some advanced topics. Hence, in this step we can cover the below techniques & try to master them before moving ahead.

- Support Vector Machines:This supervised learning model is used for data classification and regression analysis. An SVM model can efficiently do non-linear classification using a kernel trick, where inputs are mapped into high-dimensional feature spaces. Supervised learning is not possible if data is not labelled, hence in those cases unsupervised learning approach needs to be followed. The data clustering algorithm used for unsupervised learning is known as support vector clustering.
- Kaggle Titanic Competition
- Dimensionality Reduction

**Step 7: Deep Learning in Python**

Deep learning plays a crucial role in Machine learning, as it helps to build the neural network for the AI.The deep learning works as the building block for many of the exciting technology in diverse areas like automobile industry & robotics. Using Python, we can create our own neural networks for machine learning. Python also comes with 2 of the leading deep learning libraries.

Caffe: This library was created considering the points speed, expressions and modularity. Due to these features, Caffe is already being used for research projects, large scale industrial applications and event for startup-prototypes.

Theano: This Python library is a numerical computation library, which allows the users to perform multi-dimensional mathematical evaluations. The users can perform various actions like defining, optimizing and evaluating very efficiently.