Essentially Python is Simple Programming Language on the planet . Python is an object-oriented programming language made by Guido Rossum in 1989. It is in a perfect world intended for fast prototyping of complex applications. It has interfaces to numerous OS system calls and libraries and is extensible to C or C++. Numerous expansive organizations utilize the Python programming language incorporate NASA, Google, YouTube, BitTorrent, and so forth.
Python is broadly utilized as a part of Artificial Intelligence, Natural Language Generation, Neural Networks and other advanced fields of Computer Science. In order to know why Python is ahead of R in Machine Learning see here for the details. Python had deep focus on code readability and this class will show you python from essentials.
Attributes of Python
- It gives rich data types and easier to read syntax than some other programming languages.
- It is a platform independent scripted language with full access to operating system API’s
- Contrasted with other programming languages, it permits more run-time flexibility
- It incorporates the fundamental text manipulation facilities of Perl and Awk
- A module in Python may have at least one or more classes and free functions
- Libraries in Pythons are cross-platform compatible with Linux, Mac, and Windows
- For building huge applications, Python can be compiled to byte-code
- Python supports functional and structured programming and in addition OOP
- It supports interactive mode that permits interacting Testing and investigating of pieces of code
- In Python, since there is no compilation step,editing, debugging and testing is fast.
Role of Python in AI :
Python is a language with the best compilation of Lisp and Java both. According to Norvig is his text comparing Lisp to Python, these two languages are fundamentally the same as each other with some minor contrasts. There likewise exists JPython, offering access to the Java GUIs. This is the reason behind Peter Norvig picking JPython to interpret his projects from his AI book. As JPython enabled him to have convenient GUI demos, and portable http/ftp/html libraries. Accordingly, it is great to use as AI language.
Advantages of Utilizing Python over the Other Programming Languages for AI
- Great quality documentation.
- Platform skeptic, and present in for all intents and purposes circulation.
- Simple and quick to learn in contrast with some other OOP language.
- Python has many image intensive libraries like Python Imaging Library, VTK and Maya 3D Toolboxs, Numeric Python, Scientific Python and many numerous different tools.
- Python is extremely very well designed, quick, robust, portable, and scalable. These are clearly the most imperative elements for AI applications.
- Helpful for a truly wide scope of programming errands from little shell contents to big business web applications to scientific uses.
- Last however not the slightest, it is Open Source! Great community group bolster accessible for the same.
Brush up your Python skills – If you have already some Programming experience
Because Python is extremely popular, both in the industrial and scientific communities, you will have no difficulty finding Python learning resources. If you are a complete beginner, you can start learning Python using online materials, such as courses, books, and videos. For example:
Different online Courses to Learn Python [taught by Python Experts]
3. Complete Python Masterclass [BEST]
I think on the off chance that you need to learn Python inside 1 month.Then the most ideal way is learning without anyone else input and spending just about 5-6 hours day by day to code in Python.First take in the basics,if you are already basic programmer then it would take around 10 days and once you get a tight get at essentials at that point code every one of the nuts and bolts data structures and algorithms.Learning python by your own particular is something exceptionally easy.
In my case, I have learnt it on my own.The only thing which matters is from what source you are attempting to learn.If the source isn’t alright then unquestionably you will lose your enthusiasm as time runs on.And learning with practical usage has dependably been the most ideal approach to take in these technologies.Listed beneath are the best sources to learn python all alone:
 Hackr.io : Unarguably extraordinary compared to other online source I discovered which has an extremely interesting activity .What they have done is they succinctly snatched all the best online recordings sources accessible to take in these innovations and installed it into a solitary platform.
 Python.org : This is the unparalleled pioneer to gain from the documentation of the python organisation.But it could be impressively intense for the apprentices.
 Automate the Exhausting Stuff with Python : It is a standout amongst other book for apprentices for quick and quality stuff.
The next step is to Learn Essential machine learning skills
Subsequent to getting comfortable in essential Python programming skills added to your repertoire, you’re prepared to get fundamental machine learning abilities. A useful way to deal with learning is all that anyone could need to begin; notwithstanding, in the event that you are occupied with diving deep into the subject, be prepared to contribute maybe many hours of learning.
One effective approach to obtain skills is with online courses. Andrew Ng’s Coursera Machine Adapting course is an incredible alternative. Other online training worth looking at include:
(You can likewise watch machine learning streams on LiveEdu.tv to figure out the subject.)
Before getting started small project we need to choose the Python IDEs which are suitable for learning Machine Learning. I have listed 3 Best Free Python IDE for machine learning.
Final step is to go through the Video series for the Introduction to machine learning in Python:
What is Machine Learning? How does it works?
Python and machine learning – Stanford Scholar
Here there are 72 Videos series from Stanford Scholar.
Below are the topics I covered with estimated time in minutes from above 72 Videos (Refresher)
Intro to Python 1 Getting Started by Stanford Scholar 7:23 min
Intro to Python 2 Data Types by Stanford Scholar 5:04
Intro to Python 3 Loops by Stanford Scholar 5:21
Intro to Python 4 Functions by Stanford Scholar 4:28
Intro to Python 5 Lists by Stanford Scholar 6:27
Intro to Python 6 Classes by Stanford Scholar 2:47
Intro to Python 7 File I/O by Stanford Scholar 6:43
Python: 1.1 – Number, Operators and Operations by Stanford Scholar 10:16
Python: 1.2 – Basic Input and Output Operators by Stanford Scholar 3:04
Python: 1.3 – Strings by Stanford Scholar 7:17
Python: 1.4 – Lists by Stanford Scholar 15:26
Python: 2.1 – Control Flow by Stanford Scholar 6:37
Python: 2.2 – Functions by Stanford Scholar 19:58
Python: 2.3 – Classes by Stanford Scholar 16:16
Python: 2.4 – Recursion by Stanford Scholar 12:01
Python: 2.5 – Efficiency and orders of growth by Stanford Scholar 3:46
Python: 2.6 – Simple Algorithms by Stanford Scholar 8:51
Python: 3.1 – Errors and Exceptions by Stanford Scholar 7:01
Python: 3.2 – Multi threading by Stanford Scholar 13:34
Python: 3.3 – Network Sockets by Stanford Scholar 15:35
Python: 4.0 – Good Coding Practices by Stanford Scholar 12:18
Algorithms: 1.2 Linear and Binary Search by Stanford Scholar 7:47
Algorithms: 1.3.1 A Intro to Sorting and Comparison Sort by Stanford Scholar 11:47
Algorithms: 1.3.2 Sorting – QuickSort and MergeSort by Stanford Scholar 11:15
Algorithms: 1.3.3 Linear Sort by Stanford Scholar 10:04
Algorithms: 1.5 Topological Sorting and Strongly Connected Components by Stanford Scholar 6:59
Algorithms: 1.7 Minimum Spanning Tree by Stanford Scholar 7:40
Algorithms: 2.1 Designing Algorithms: Introduction by Stanford Scholar 8:19
Algorithms: 2.2 Greedy Algorithms by Stanford Scholar 8:07
Algorithms: 2.3.1 A Divide and Conquer Karatsuba by Stanford Scholar 10:36
Algorithms: 2.3.2 Divide and Conquer Closest Pair Points by Stanford Scholar 13:47
Algorithms: 2.4 Recursion by Stanford Scholar 14:54
Algorithms: 2.5 Dynamic Programming by Stanford Scholar 9:57
Algorithms: 2.6 P, NP and NP complete by Stanford Scholar 15:33
Algorithms: 3.1.1 Introduction to Data Structures by Stanford Scholar 3:19
Algorithms: 3.1.2 ADT Stacks and Queue by Stanford Scholar 3:02
Algorithms: 3.2 Arrays by Stanford Scholar 3:59
Algorithms: 3.3 Hash Tables by Stanford Scholar 5:00
Algorithms: 3.5.1 Stack by Stanford Scholar 14:04
Algorithms: 3.6 Trees by Stanford Scholar 9:21
Algorithms: 3.7 Heaps by Stanford Scholar 4:18
Algorithms: 3.8.1 AVL TREES by Stanford Scholar 10:19
Practical Machine Learning: 1.1 – Python tools for Machine Learning by Stanford Scholar 4:35
Practical Machine Learning: 1.2 – Setting up a Python Environment by Stanford Scholar 2:08
Practical Machine Learning: 2.1 – Overview Of Machine Learning by Stanford Scholar 4:37
Practical Machine Learning: 2.1 – Overview of Machine Learning by Stanford Scholar 4:44
Practical Machine Learning: 2.2 – Data Cleaning by Stanford Scholar 7:40
Practical Machine Learning: 2.3 – Feature Engineering by Stanford Scholar 13:10
Practical Machine Learning: 2.4 – Parameter Tuning by Stanford Scholar 9:02
Practical Machine Learning: 2.5 – Concept Learning by Stanford Scholar 4:51
Practical Machine Learning: 2.6 – General to Specific Ordering by Stanford Scholar 12:29
Practical Machine Learning: 3.1 – Overview of Algorithms by Stanford Scholar 6:19
Practical Machine Learning: 3.2 – Exploratory Data Analysis by Stanford Scholar 7:51
Practical Machine Learning: 4.1 – Intro To Linear Regression by Stanford Scholar 7:18
Practical Machine Learning: 4.2 – Multivariate Linear Regression by Stanford Scholar 6:10
Practical Machine Learning: 4.3 – Regularization and Model Evaluation by Stanford Scholar 9:08
Practical Machine Learning: 5.1 – Introduction and Motivation for Classification Algorithms by Stanford Scholar 5:19
Practical Machine Learning: 5.2 – Logistic Regression by Stanford Scholar 12:39
Practical Machine Learning: 5.3 – Support Vector Machine by Stanford Scholar 21:54
Practical Machine Learning: 5.4 – Artificial Neural Networks by Stanford Scholar 15:34
Practical Machine Learning: 5.6 – Naive Bayes by Stanford Scholar 9:55
Practical Machine Learning: 6.1 – Clustering Motivation by Stanford Scholar 4:55
Practical Machine Learning: 6.2 – K Means by Stanford Scholar 10:34
Practical Machine Learning: 6.3 – K Nearest Neighbors by Stanford Scholar 10:26
Practical Machine Learning: 6.4 – A Hierarchical Clustering by Stanford Scholar 8:20
Practical Machine Learning: 6.5 – B Hierarchical Clustering Example by Stanford Scholar 9:02
Practical Machine Learning: 6.6 – Metrics by Stanford Scholar 8:26
Practical Machine Learning: 7.3 – Cross Validation by Stanford Scholar 9:33
Practical Machine Learning: 7.4 – Cross Validation Based Model Selection by Stanford Scholar 11:30
Practical Machine Learning: 7.5 – Error Analysis Classification Measures by Stanford Scholar 13:57
Practical Machine Learning: 7.6 – Cross Validation Error Analysis by Stanford Scholar 8:34
Practical Machine Learning: 7.7 – Estimating Hypothesis Accuracy by Stanford Scholar 10:46
In this post, you found well ordered how to finish your first machine learning venture in Python.
You found that finishing completing a small end-to-end project from loading the data to making expectations is the most ideal approach to get comfortable with another platform.
Your Next stage
Do you work through the instructional exercise?
Work through the above instructional exercise.
Rundown any inquiries you have.
Inquiry or research the appropriate responses.
Keep in mind, you can utilize the help(“FunctionName”) in Python to get help on any capacity.
Do you have an inquiry? Post it in the remarks underneath.