What is machine learning with python?
Machine learning is a subset of artificial intelligence that teaches the computer how to learn to deal with data and modify the data based on previous logical conclusion.
Python is an easy language to begin with and has lots of modules and libraries to choose from when you want to feed the machine learning algorithms into.
Also, it is the most popular programming language and is used extensively in designing and writing machine learning algorithms.
Unlike R, it is easier to learn machine learning with Python.
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Is Python necessary for machine learning?
To implement machine learning algorithms, we need a programming language.
One can go with any platform of individual choice: R, Matlab, Scala, Java, C/C++ or Python and all of them have their own pros and cons.
Python is the most popular and preferred choice because it is easy and flexible.
Thanks to its vast dedicated libraries (NumPy, Pandas, Scikit- learn, TensorFlow) that helps data scientist perform ML tasks relatively easy.
What are the machine learning tools?
Machine learning tools are used by data scientists for data collection, preparation, building ML models, training and application deployment in a typical production environment.
Some of the most used Python tools for machine learning:
- Tensorflow (used in almost every Google applications for machine learning)
- SciKit-Learn (free software ML library for Python)
- Keras (Open source library written in Python for neural networks)
- Jupyter Notebook (for interactive computing)
- Theano ( Python library for fast numerical computation)
- Shogun (Open source ML library and provides vast ML methods)
- Apache Spark MLlib ( machine learning and statistical algorithms)
What is a machine learning library?
Python has numerous libraries that are used to implement Machine Learning.
- NumPy (Python library for scientific computing)
- Pandas (Open source Python library for data structures and data analysis)
- Matplotlib (Python library for interactive data visualization)
- Scikit-learn (well established algorithms for supervised and unsupervised learning)
- SciPy (Data optimization and numerical routines)
- Nilearn ( Statistical learning on neuroimaging data)
- Theano ( Python library for numerical computing)
- Seaborn (Data visualization library built on Matplotlib)
- TensorFlow ( library for ML applications such as neural networks)
- PyTorch ( open source ML library for natural language processing)
How to learn machine learning with Python?
Below are our best picks of machine learning with python courses online if you are looking to learn from MOOCs and online learning platforms.
Coursera is one of the best MOOC’s that offers some great course to get machine learning mastery with Python.
It is one of the best organised courses at Coursera to learn machine learning with Python.
Also, it gives you an overview of how to effectively use Scikit-learn library on ML.
You can either audit the course for free or opt for a subscription if you want to earn a certificate at the end.
The course is of intermediate level and requires you to have a good programming knowledge of Python.
Since it focuses on applied machine learning aspects rather than statistics part of it, AndrewNg machine learning course can come handy if you want to brush up the math part.
- Fundamentals of Machine Learning
- How to implement KNN’s algorithm using the Scikit-learn library
- Supervised machine learning methods for both classification and regression
- How to use regularization to avoid overfitting
- How to evaluate and model selection methods to optimise the performance of ML methods
- Advanced supervised machine learning methods : random forests, gradient boosted trees, neural networks, etc
- The practical limitations of predictive models
- Identify the difference between classification and clustering techniques
Machine Learning with Python from IBM is equally a good alternative course to learn both machine learning and python.
If you are looking for advanced machine learning with Python courses at Coursera, these specializations are worth checking:
If you want an MOOC alternative, Udacity Nanodegree programs are worth it but there is a cost attached to it.
Their Nanodegree are popular for their well-crafted projects, mentors, project reviews, feedbacks, slack community and it provides you the perfect framework to teach yourself what you intend to learn.
This course is taught in a typical Python environment along with popular frameworks like Skikit-learn, TensorFlow and Keras.
According to the Udacity website, it will take you 3 months to complete the program if you invest a study hours of 5-10 hrs every week.
- Python Programming , configuration of development environment and standard library functions
- Descriptive Statistics: Data visualization, normal and sampling distribution
- Inferential Statistics: Data analysis process, NumPy and Pandas for 1D and 2D arrays and Data modelling
- How to use recall rate and similar indicator to test and measure data performance
- 4 industry relevant projects
Their Python Foundation Nanodegree Program is best if you want to learn Python programming fundamentals and how to work on NumPy and Pandas to handle and manipulate data.
Udemy is another popular online learning platform with pay per course model and instructor led course tutorials.
Also, have inclusive features such as 30 days money back guarantee, lifetime access to the course material, a certificate on completion and you can access the course in both Andriod and iOS
If you are opting to learn machine learning with python at Udemy, this Bootcamp course from Jose Portilla is a popular choice.
It teaches you how to use the power of Python to analyse data, create data visualizations and use powerful ML algorithms.
You get detailed code notebooks for every lecture and the course has got close to 2 lakh enrollments.
These udemy ML course are equally popular to learn Machine learning concepts:
EDX is another MOOC platform that has a handful of professional certification program in Data Analysis and Statistics.
It is an introductory course offered by IBM and covers machine learning basics with Python.
- Supervised vs Unsupervised Machine Learning
- How Statistical Modeling relates to Machine Learning, and how to do a comparison of each.
- Different ways machine learning affects society
Similar to Coursera, you can study the course for free else you have to pay to get a verified certificate.
Edureka’s machine learning certification program is an Instructor-led live online course type and you will receive a course completion certificate at the end.
There are 34 case studies and 3 projects as part of their machine learning certification training using Python.
- Machine learning algorithms: Regression, clustering, decision trees, random forest, Naive Bayes, etc.
- Introduction to data science
- Machine learning topics: Supervised, Unsupervised and Reinforcement learning
- Statistics concepts, time series analysis, model and selection boosting, etc
- Hands-on with cross validation and AdaBoost
Best Machine Learning with Python Tutorials:
These are some of the best online resources in the form of simple tutorials and blog posts to learn machine learning with Python.
Especially, if you are an absolute beginner in ML and want to learn it for free.
The tutorial is intended for those looking to learn python basics and how to develop ML techniques like classification, clustering, etc and solve data-driven problems using Python and it packages.
The tutorial starts with an introduction to machine learning, Python language and how to setup Python and its packages.
Python-course.eu has lots of free comprehensive online tutorials on topics related to ML and Python that you can self study at your own pace.
This machine learning with python tutorial covers topics such as the different ML classifiers, different neural networks with Python packages, Python implementation with text classifications, intro to TensorFlow and much more.
A basic understanding or Python is a pre-requisite before you start studying this course.
The course aims at teaching you core parts of machine learning including theory, applications and how supervised, unsupervised and deep learning algorithms work.
It means you will be practically working on these ML algorithms including the coding and math part of it and decipher it know the pros and cons attached with each algorithms.
On a side note, this website has a handful of tutorials exclusively on Python programming, if you want to master Python first.
Scikit-learn is a free machine learning library used in Python.
This simple beginner friendly blog talks about how to easily apply various ML algorithms like svm, random forests, k-neighbours with the help of scikit-learn library in Python.
Make sure you are aware of Pandas, Seaborn and Matplotlib basics in order to understand the blog posts.
The tutorials teach Ml with the help of a supervised learning algorithm called KNN (K-Nearest Neighbour) with Python.
You will be implementing the KNN algorithms on the famous Iris dataset.
YouTube Playlists – Machine Learning with Python Tutorial:
Let us know in the comments sections what are your best ways to learn machine learning with Python?
Happy Data Learning!