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7 Steps to Master ML with Python

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7 Steps to Master ML with Python

Python – Often a beginner in machine learning gets lost in the sea of available resources to master this subject. There are so many feely available Python machine learning resources as well a plethora of paid resources in both offline as well as online mode. In this blog, we’ll try to help a newcomer in machine learning to chart out a plan to master this subject

While learning any new thing, the first step is often the hardest to take, and things get more complicated when we are offered too much choice in terms of direction. So let’s help a newcomer having minimal knowledge of machine learning in Python or to understand a python tutorial to become a knowledgeable practitioner in just 7 steps entirely. To make matters more clear, I assume that you are not an expert in either Python or Machine learning and just have some knowledge of Python’s popular machine learning, scientific computing, or data analysis libraries.

Here are the seven steps to master ML using Python.

Step 1🡺 Learn and master basic Python skills

Step 2 🡺 Build a strong foundation in Machine Learning Skills

Step 3 🡺 Develop a working knowledge of popular Scientific Python Packages

Step 4 🡺 Start developing basic ML problems in Python

Step 5 🡺 Explore intermediate ML topics

Step 6 🡺 Start coding advanced ML problems

Step 7 🡺Begin learning Deep learning problems and start coding them in Python

Let us deep dive into each of these steps.

The first step to master Machine learning using Python is to know Python in and out. You have to form a strong foundation of programming in general and Python in particular. Python is very popular both as a general purpose programming language, as well as a specialized programming language for both scientific computing and machine learning. The internet is full of Python beginner’s tutorials. You must choose a course as per your level of experience in both Python and generic programming.

The first baby step is to have a python installation ready. For that, you need to install Python. The two best IDEs for Python for scientific or machine learning are PyCharm and Anaconda. I would recommend using Anaconda since it is not only well supported and documented, it has a very easy to use GUI and offers a plethora of editors to do coding. It is used in industry for Python development on Linux, OSX, and Windows, complete with the required packages for machine learning, including numpy, scikit-learn, and matplotlib. Anaconda also contains an iPython Notebook that offers an interactive environment to code and see the display in real time. I would suggest moving to Python 3 since Python 2.7 has now gone obsolete. You must always refer to Python official documentation for any doubt.

The second step is to build a strong base in Machine learning. You must brush up your knowledge on probability, statistics, set theory and mathematics to understand Machine learning since all machine learning algorithms use these 4 branches of science. You do not need to gain an intimate understanding of machine learning algorithms but should have a working knowledge of how an ML algorithm works. You do not need to master all theoretic aspects of each and every ML algorithm but should have basic to intermediate knowledge of how the algorithm works.

The third step is to develop a working knowledge of popular Scientific Python Packages. Once you are done with step 1 and step 2, you must be fairly confident on coding any problem using Python and can also give a shot on providing its theoretical explanation. Now, it is the time to move to actual coding for machine learning problems. Since ML is a specialized and complex topic, there are a number of open source libraries generally used to facilitate practical machine learning. Some of the popular libraries that are heavily used in ML are as follows:

  1. numpy – NumPy is the de-facto python library when it comes to large multi-dimensional arrays and matrix processing. It has a built -in collection of high-level mathematical functions to work on matrix, vectors and scalars. It is hugely used for fundamental scientific computations in Machine Learning. Apart from that, it is also useful for linear algebra, Fourier transform, and random number capabilities.
  2. pandas – Pandas is a popular Python library for data analysis, data extraction and preparation. Pandas library provides many inbuilt methods for data grouping, data subset combining and data filtering.
  3. Scipy – SciPy is a popular library that contains different modules for optimization, linear algebra, integration and statistics.
  4. scikit-learn – Scikit-learn is the most popular ML library to implement classical ML algorithms such as random forest, naive bayes, decision trees etc. It internally uses NumPy and SciPy. Scikit-learn has inbuilt support for most of the supervised and unsupervised learning algorithms. Scikit-learn is also handy for doing data-mining and data-analysis.
  5. matplotlib – Matplotlib is a very popular Python library for data visualization. It is basically a 2D plotting library. It contains a module named pyplot that makes it easy for programmers for plotting as it provides features to control line styles, font properties, formatting axes, etc.
  6. Theano – Theano is a popular python library that is basically an advanced mathematics processing library. It is used to define, evaluate and optimize mathematical expressions involving multi-dimensional arrays in an efficient manner. It does so by optimizing the utilization of CPU and GPU (if available).
  7. TensorFlow – TensorFlow is another highly advanced open-source library to perform complex numerical computation. It was developed by the Google Brain team. It can train and run deep neural networks that can be used to develop several AI applications. TensorFlow is widely used in the field of deep learning research and application.
  8. Keras- It is a very popular Machine Learning library for Python that has a high-level neural networks API which runs on top of TensorFlow, CNTK, or Theano. It can run seamlessly on both CPU and GPU. Keras makes it really for ML beginners to build and design a Neural Network and go for easy and fast prototyping.
  9. PyTorch – It is a popular open-source Machine Learning library for Python based on Torch (Torch is C based open-source Machine Learning library). It has an extensive choice of tools and libraries that support Computer Vision, Natural Language Processing (NLP) and many more ML programs.

Also check out some of the other Machine learning libraries and tools in the below image

The fourth step is to work on basic ML problems in Python. Now is the time when you can start implementing machine learning algorithms with Python’s de facto standard machine learning library, scikit-learn. You will find out that most of the ML tutorials and exercises are done on iPython (Jupyter) Notebook, which is an interactive environment for executing Python. The benefit of iPython notebooks is that they can be viewed online or downloaded and interacted with locally on your own computer. You can begin by going through important functions of each ML algorithm in scikit-learn libraries and then take up each class of ML problems beginning with simple linear regression. You can solve each problem on a medium dataset using functions in scikit-learn. In the process, you will learn how to preprocess a data set, split a data set, create a ML pipeline, train the model, display the metrics and finally predict using the model on unseen values.

The fifth step is to focus on intermediate level machine learning topics and start coding them in Python. Post step 4; you have built your foundation in ML. It is time to move on to the next level. Now, you can proceed towards some more in-depth explorations of the intermediate problems involving advanced algorithms. You can start with k-means clustering, one of the most popular unsupervised machine learning algorithms that is used in clustering. You can then focus on the k-nearest neighbor’s algorithm. Post that, you can switch to classification using advanced classification methods such as decision trees and  logistic regression.

The penultimate step is to move towards advanced machine learning topics and start coding them in Python. Now it is the time for some advanced stuff. You can study support vector machines and solve classification problems on intermediate to large data sets. SVM is both a linear and non-linear classifier that does classification by doing complex transformations of data into higher dimensional space. In the process, you would also learn about various types of Kernel deployed, hyper parameters and their tuning while studying SVM. Post SVM, you can choose random forests which are ensemble classifiers.

Then you can move towards PCA or Principal Component Analysis, which is a particular form of unsupervised dimensionality reduction. Dimensionality reduction is a method for reducing the number of variables in a problem domain.

Towards the end, you will realize that you have not only gone through most of the ML algorithms ranging from basic complexity to advanced complexity. And you have also examined some additional machine learning support tasks such as dimensionality reduction, parameter tuning and model validation techniques. Now, you have a useful toolkit for yourself.

The last but the least step is to start learning about Deep Networks and coding bigger more complex problems using Deep neural networks in Python. Deep learning builds on advanced neural network research of several decades. To begin with, you can explore Theano and Caffe , two leading contemporary Python deep learning libraries. These have several inbuilt examples and tutorials on constructing simple to complex deep neural networks. Once you have mastered the basics, you can move towards real world multi disciplinary problems such as building a content moderation system.

If you follow this action plan, you can confidently take on real work problems on Machine Learning problems and successfully solve them. Best of luck!

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