I will be sticking with Python for Machine Learning and Data science as Python comes with a huge amount of inbuilt libraries. What makes Python favourite among programmers is that it is powerful and is easy to implement. Also, most of the programmers don’t want to spend much of their time on debugging the code for syntax errors, they want to devote most of their time on their algorithms and heuristics related to these subjects.
Treating you as a beginner in this field I would first like to suggest a path which will make things clear in your mind as what you are going to learn and later you can decide with your rational mind that if it is going to help you in your career or not? Be clear with one thing that you cannot learn data science by just learning Python. Data science with Python include many things like, first is Python which is basically a tool for you to perform several task, secondly you need to learn database technologies because you cannot do anything related to data without learning database technologies, third is mathematics and statistics which are going to be the building blocks of your logic and algorithms. Lastly it is machine learning. If you can learn all the above mentioned things, then you will be able to grab a high paying job as a data scientist. But having dearth of knowledge in any of these is probably not going to help you much. So how should you start or what should be your planning to move towards your final goal? You should start with learning the language first and then move to the other parts.
So below are the resources from where you can learn Python for Machine Learning/Data Science:
1. Learn from Books: There are many wonderful books on internet which teaches deeply about this. I am going to list a few of them along with their USPs:
- Building Machine Learning Systems with Python: The author of this book has used a very simple language so that the reader can understand the whole concept. Not only the concepts would be clear but author has also used several examples which make learning more interesting and practical. Python assignment help can be easily taken from this book as it helps you to practice the taught concept. However, one can get this help from many online resources also available on net. Google is God for everyone especially for students.
- Introduction to Machine Learning with Python: This book covers one of the most powerful python libraries available for Machine Learning called SciKit in detail. This book empowers you to build and run your own machine learning model. It helps you in assignments related to text processing in Python which will make you an expert in text mining are wonderfully presented in this book.
- Mastering Python for Data Science: This book covers the topics like building accurate and high performing machine learning model. This book also contains the best contents on NumPy and Pandas. Also, author focuses on the basics and covers advance topics like recommendation systems etc.
- Think Stats: This is the only book that helps you learn statistics as well as Python.
- Programming collective intelligence: Ever wondered how Ecommerce sites suggest products based on your search history? This book helps you learn this. The examples present in this book give real world basic to abstract concepts like collaborative filtering and Bayesian classification.
2. Learn from Tutorials: If you don’t like to learn from books and learn better by watching videos, below are some important resources for you.
- Codeacademy: The motto of their teaching is ‘Learn with doing it’, which is the effective way to learn otherwise there is a high probability that you are going to forget the topics and concepts.
- Kaggle: It offers you the step by step assistance that makes you learn Python well. Not just this, Python assignment help can be taken from this amazing tutorial that makes you develop concepts from basics to the advance level.
3. Learn from MOOCs:
Virtual learning has become a trend nowadays. Sites like Edx, Courseera, Udacity etc. has become the biggies in this field. The best thing about MOOCs is that you get to learn from best teachers in the world while sitting on your couch. Below is the list of good courses to get enrolled in Coursera:
- Applied Machine Learning in Python by University of Michigan.
- Applied Data Science with Python by University of Michigan.
- Structuring Machine Learning Projects: This course is for those who have some knowledge of Machine Learning and prefer learning things by doing projects.
Also, many universities are contributing to open course by uploading the videos of their classroom programs for free. For instance some courses from MIT Open Course Ware are:
- Mathematics of Machine Learning: This course deals with the most basic concept of identification of patterns in the data.
- Prediction: Machine Learning and Statistics: Using machine learning and statistics to achieve most important endeavor in scientific research, Prediction.
- Machine Learning: This is the introductory course which mainly deals with many basic concepts, techniques and algorithms which are being used in machine learning.
- Algorithmic aspect of machine learning: As the name suggests, this course is all about the algorithms used in machine learning.
4. Learn from Applications: Last, if you are one of those people who doesn’t have much time but want to learn new topics to accelerate your growth then applications are the thing for you. One of these applications is SoloLearn. This application has got space in my mobile. It is too good; they also help you learn by doing things practically, hence the better learning.
I really found these resources very helpful for making us have a clear understanding of using python. Now it just depends on your area of interest. No matter if you are inclined towards web intelligence, natural language processing or want to become a data scientist, these resources are going to be a major help for you. Hope you have a less painful time learning how to use python for data science and machine learning.
If you feel that there can be any other resources that i haven’t talked about, please feel free to share your resources and ideas with us.