The most widely used language for machine learning professionals is python. It has many rich attributes, which are given below.
- Python is an object-oriented programming language
- It is a high-level language
- There are many libraries which are available for python
- The data structures are user friendly
- The syntax is very easy
Now let us look at the seven steps to learn python machine learning.
Step 1: Basic Python Skills
Some basics are needed to use python for machine learning. As machine learning and all the scientific calculations are using python since the start, it not difficult to start python machine learning.
There is a platform for python known as Anaconda, which is used for machine learning and scientific computation. The best part of python is that it is supported by all of the popular operating systems like Linux, Windows, and OSX. There are different libraries like NumPy and scikit-learn that come with the package. The most dominant version is python 2.7. There are many tutorials and books that are available to excel your python skills.
Step 2: Foundation of Machine Learning Skills
If you are a beginner, you will feel that machine learning uses many technical concepts. Machine learning is based on the fundamental concepts that we have studied in our school.
If anyone is willing to get into data science, there are certain tasks that are done by a data scientist, which include the knowledge of machine learning. Before starting to experiment with different algorithms, it is necessary to gain enough theoretical knowledge. Some concepts are
- Data analysis
Step 3: Python Packages
The basics of python machine learning are necessary to start using python machine learning packages. There are various libraries or packages available in python which will improve your results. Though inbuilt python libraries are enough for machine learning, you can also import different libraries. Some of the python libraries which are used in machine learning are:
- Pandas – This includes structures like data frames
- Matplotlib- It is a 2D plotting library
- Scikit-learn- Used for data analysis and data mining
To learn in detail about these packages, the following books can be referred:
- Scipy Lecture Notes
- 10 minutes to Pandas
Python Machine Learning
As we have learned about the libraries and basics of python and machine learning, we can start implementing different ML algorithms using the scikit-learn library. IPython Notebook provides an interactive environment to use python algorithms. These are available offline as well as online. To excel your skills, the following approach can be used.
- Expertise scikit-learn by working on projects
- Start comparing various models
- Practice scikit-learn model’s execution
Step 5: Machine Learning Topics
After learning scikit-learn, we can start with advanced levels by using various machine learning algorithms. Some easy and effective algorithms are given below:
- Linear Regression: This is a linear approach used for establishing a relationship between X, and Y. Y denotes dependent variables, whereas x denotes one or more independent variables.
- Logistic Regression: In this, the dependent variable is generally categorical. Binary logistic regression and multinomial regression are two different models available.
- K-means Clustering: This is used by unsupervised learning algorithms. In this n number of observations are partitioned into k clusters.
Step 6: Machine Learning Topics
A journey with adventures is always interesting. In our journey, there will be adventures as we start learning advanced machine learning concepts. These concepts will make you more skilled in classification. Here are some of the techniques which should be mastered.
- Kraggle Titanic Competition
- Dimensionality Reduction: It is a method used for reducing the number of variables used in the problem.
- Support Vector Machines: This model is used for data classification and regression analysis. Support vector clustering is a data clustering algorithm used for unsupervised learning.
Step 7: Python Deep Learning
In machine learning, deep learning plays an important role. It helps in building a neural network for artificial intelligence. It works as a building block for many exciting technologies in different areas like robotics and automobile industries. We can even create our own neural networks by using python. There are two deep learning libraries that come with python.
- Caffe: This was created considering modularity and expressions. Caffe is used for many research projects and even for startup prototypes.
- Theano: This is a numerical computation library, which allows the user to perform multi-dimensional mathematical evaluations. Defining, optimizing, and evaluation can also be performed.
Using these steps will surely help you master machine learning with python. Try and practice them as much as you can, and you will be able to land a great job in the field.