Especially, Python is an object-oriented, interpreted programming language. Its advanced concept in data structures, blended with dynamic binding and dynamic typing, makes it a most fascinating language to avail as a glue or scripting language to bridge existing services or components and for Rapid application development.

Aspirants often commit certain mistakes which are quite common while using this high-level programming language. In this article, let’s discuss the 10 most atrocious Python mistakes aspirants often make. 

The Top 10 Awful Python  Mistakes Newbies Make

1. Defining Main Function in Python

This is the most common mistake made by coding aspirants. As mentioned in the introduction part since Python programming is a high-level scripting language, one can define functions that could be called in REPL mode. 

Example: 

def super_fun()

print (“Good morning”)

super_fun()

With the help of the above example, we can execute the super_fun function when it’s called from the CLI python no_main_func.py. However, if the programmer wants to use the code again as a module in a book, what would be the case?

No main func

import no_main_func

Good morning

The super_fun is now executed accordingly when the script is imported. This is a simple example, but if you are executing some heavy computational operations, or giving rise to an activity that creates multiple threads. In this case, Aspirants want to avoid running the code automatically on import, so this is how one can ward off the same.

def super_fun():

print(“Good morning”)

if_name_==”_main_”:

# execute only if run as a script

super_fun()

As you can see, unexpected behaviors have been prevented when you import it as a module. 

2. The boolean mess

Like in every other programming language, the doubt of what’s supposed to be contemplated as a Boolean “true” value is a most common confusion in Python too. 

Let us understand with the help of an example: 

>>> 0 == False

True

>>> 0.0 == False

True

>>> [] == False

False

>>> {} == False

False

>>> set() == False

False

>>> bool(None) == False

True

>>> None == False

False

>>> None == True

False

In the above example, the zero value for any numeric data type is appraised to be false. However, empty collections such as sets, lists, or dictionaries are true. This can be tedious as a variable can be kept undefined and later incorporated in a contrast that will deliver an unpredicted outcome. 

3. Misusing Python Scope Rules

Python scope resolution is built on the LEGB rule, that is, contemplated as an abbreviation of Local, Enclosing, Global, and Built-in. Seems easy-peasy, isn’t it? Actually, there are certain subtleties to the way it runs in Python programming language that delivers coders the common error and this is one of the most atrocious python mistakes that freshers do. 

4. Misunderstanding Function Arguments by Reference And Value

The high-level programming language Python has a weird way of incorporating arguments in functions and methods. Aspiring coders who are shifting to Python from other languages like C++ or Java may misunderstand the way that the interpreter runs with arguments.

5. Class Variables Confusions

Object-oriented programming intends to put together problems in a way that reflects the real world, however, it can become tedious to newbies in the coding space. 

6. Updating a list while iterating over it

Removing a thing or any item from an array or list while iterating over it could be problematic, which is familiar to the majority of experienced software developers. However, most of the time it becomes cumbersome for aspirants. Sometimes professional Python developers could face problems by this in code that is more complicated. 

7. Ineffectiveness in understanding the differences between Python 2 & Python 3

Freshers fail to understand the actual difference between Python 2 & Python 3 since the two versions are quite similar. In Python 3, the exception object could not be accessed beyond the scope of the except block. The primary reason is that it would grasp a reference cycle with the stack frame in memory till the garbage collector continues to operate.

8. Float Data Types

This is one more addition to the 10 most atrocious Python mistakes. Beginners misunderstand float as a simple type. For instance, the below-mentioned example highlights the difference between defining the identifier of float types and simple types. 

>>> a = 10

>>> b = 10

>>>

>>> id(a) == id(b)

True

>>> c = 10.0

         >>> d = 10.0

>>>

>>> id(c) == id(d)

False

>>> print(id(a), id(b))

9788896 9788896

>>> print(id(c), id(d))

140538411157584 140538410559728

Find another example below, an easy arithmetic operation that is simple to solve, but you will get the unpredicted results because of the comparison operator. 

>>> a = (0.3 * 3) + 0.1

>>> b = 1.0

>>> a == b

False

Programmers can avoid the same with the help of the following function mentioned in the example below: 

def super_fun(a:float, b:float):

   return True if abs(a-b) < 1e-9 else False

   if __name__ == “__main__”:

  # execute only if run as a script

   print(super_fun((0.3*3 + 0.1),1.0))

9. Perplexity over how Python binds variables in closures

Beginners often get confused over this because Python’s late binding attribute states that the values of variables exploited in closures could be looked up at the time while calling the inner function.

10. Name collision with Python Standard Library Modules

One of the interesting things about this high-level language is the opulence of library modules. If beginners are not avoiding it smartly, names get clash between one of your modules and the one which already exists in the library.  

The post The 10 Most Atrocious Python Mistakes Aspirants Often Make! appeared first on .

Overview of Python

Python used to be first launched in 1991. It is an interpreter, a high-level, typical purpose programming language. It is Object-Oriented. Designed via Guido van Rossum, Python genuinely has a graph philosophy headquartered around code readability. The Python neighborhood will grade every other’s code-based totally on how Pythonic the code is.

Read: Beginner Tips For Learning Python Programming

When to use Python

Python’s libraries allow a programmer to get started out rapidly. Rarely will they want to begin from scratch? If a programmer wants to bounce into desktop learning, there’s a library for that. If they desire to create a fascinating chart, there’s a library for that. If they want to have an improvement bar proven in their CLI, there’s a library for that.
Generally, Python is the Lego of the programming languages; locate a container with orders on how to use it and get to work. There is little that wishes to begin from scratch.

Because of its readability, Python is great for:

1. New programmers
2. Getting ideas down fast
3. Sharing code with others

Java overview

Java is old. Java is a general-purpose programming language that utilizes classes and, like Python, is object-oriented.

Java was once developed by way of James Gosling at Sun Microsystems, launched in 1995 as a phase of Sun Microsystem’s Java Platform. Java modified the internet trip from easy textual content pages to pages with video and animation.

When to use Java

Java is designed to run anywhere. It makes use of its Java Virtual Machine (JVM) to interpret compiled code. The JVM acts as its very own interpreter and error detector.
With its ties to Sun Microsystems, Java used to be the most extensively used server-side language. Though no longer the case, Java reigned for a lengthy whilst and garnered a giant community, so it continues to have a lot of support.
Programming in Java can be effortless due to the fact Java has many libraries constructed on the pinnacle of it, making it convenient to locate code already written for a precise purpose.

Who uses Python & Java?

Python is regularly used with new programmers or junior developers coming into a records science role. The large desktop mastering libraries, TensorFlow and PyTorch, are each written in Python. Python has notable statistics processing libraries with Pandas and Dask and top records visualization skills with programs such as Matplotlib and Seaborn.
Java is used a lot for net development. It is extra frequent amongst senior-level programmers. It lets in for asynchronous programming and has a respectable Natural Language Processing community.
Both languages can be used in API interactions and for computer learning. Java is higher developed for constructing internet applications. Python’s Flask library is nevertheless solely capable to build the fundamentals to a Python-based UI however is excellent for developing a Python back-end with an API endpoint.

Python vs Java in code

Let’s see how Java and Python work differently.

Syntax

Because Python is an interpreted language, its syntax is more concise than Java, making getting started easier and testing programs on the fly quick and easy. You can enter lines right in the terminal, where Java needs to compile the whole program in order to run.

Type python and then 3+2 and the computer responds with 5.

Copy

python

3+2

5

Consider doing this with Java. Java has no command line interpreter (CLI), so, to print 5 like we did above, we have to write a complete program and then compile it. Here is Print5.java:

Copy

public class Print5 {

       public static void main(String[] args) {

        System.out.println("3+2=" + (Integer.toString(3+2)));

       }

}

To compile it, type javac Print5.java and run it with java Print5.

Copy

java Print5

3+2=5

With Java, we had to make a complete program to print 5. That includes a class and a main function, which tells Java where to start.

We can also have a main function with Python, which you usually do when you want to pass it arguments. It looks like this:

Copy

def main():

  print('3+2=', 3+2)

if __name__== "__main__":

  main()

Classes

Python code runs top to bottom—unless you tell it where to start. But you can also make classes, like possible with Java, like this:

Python Class

Copy

class Number:

  def __init__(self, left, right):

      self.left = left

      self.right = right

number = Number(3, 2)

print("3+2=", number.left + number.right)

The class, Number, has two member variables left and right. The default constructor is __init__. We instantiate the object by calling the constructor number = Number(3, 2). We can then refer to the variables in the class as number.left and number.right. Referring to variables directly like this is frowned upon in Java. Instead, getter and setter functions are used as shown below.

Here is how you would do that same thing In Java. As you can see it is wordy, which is the main complaint people have with Java. Below we explain some of this code.

Java Class with Getter and Setter functions

Copy

class PrintNumber {

      int left;

      int right;

      PrintNumber(int left, int right) {

          this.left = left;

          this.right = right;

      }

      public int getleft() {

          return left;

      }

      public int getRight() {

          return right;

      }

}

public class Print5 {

      public static void main(String[] args) {

          PrintNumber printNumber = new PrintNumber (3,2);

          String sum = Integer.toString(printNumber.getleft()

                + printNumber.getRight() );

          System.out.println("3+2=" + sum);

      }

}

Python is gentle in its treatment of variables. For example, it can print dictionary objects automatically. With Java it is necessary to use a function that specifically prints a dictionary. Python also casts variables of one type to another to make it easy to print strings and integers.

On the other hand, Java has strict type checking. This helps avoid runtime errors. Below we declare an array of Strings called args.

Copy

String[] args

You usually put each Java class in its own file. But here we put two classes in one file to make compiling and running the code simpler. We have:

Copy

class PrintNumber {

    int left;

    int right;

}

That class has two member variables left and right. In Python, we did not need to declare them first. We just did that on-the-fly using the self object.

In most cases Java variables should be private, meaning you cannot refer to them directly outside of the class. Instead you use getter functions to retrieve their value. Like this.

Copy

public int getleft() {

    return left;

}

So, in the main function, we instantiate that class and retrieve its values:

Copy

public int getleft() {

    return left;

}

public static void main(String[] args) {

    PrintNumber printNumber = new PrintNumber (3,2);

    String sum = Integer.toString(printNumber.getleft()

         + printNumber.getRight() );

}

Where Python is gentle in its treatment of variables, Java is not.

For example, we cannot concatenate and print numbers and letters like “3+2=” + 3 + 2. So, we have to use the function above to convert each integer to a string Integer.toString(), and then print the concatenation of two strings.

Learn both Python & Java

Both programming languages are appropriate for many humans and have massive communities in the back of them. Learning one does now not suggest you can’t examine the other—many programmers project into more than one language. And studying a couple of cans support the appreciation of programming languages altogether.
By many measures, Python is the easier one to learn, and migrating to Java afterward is possible.

Read: Top 10 Python Training Institutes in Bangalore

The post Python vs Java: What’s The Difference? appeared first on .

“What’s the best way to learn Python?”

To learn any programming language, I believe that the first step is understanding how to learn.

The ability to learn how to learn is arguably the most important skill in computer science.

Why is it important to know how to learn?

Libraries and tools are upgraded in parallel with language evolution.

To keep up with these changes and become a successful programmer, you’ll need to know how to learn new skills.

Learn how to become a rockstar Python programmer with these learning strategies!

What makes something stick?

The following tips will help you make sure that the new concepts you are learning as a beginner programmer really stick: 1.

#You should code every day.

When you are learning a new language, consistency is key. We recommend committing to coding every single day. Muscle memory plays a big role in programming. Code every day, and you’ll develop that muscle memory.

You can start small, with 25 minutes a day, and work your way up from there.

First Steps With Python provides information on how to get started as well as exercises to help you get up and running.

#How to Write Out Your Ideas

As a new programmer, you may wonder whether or not you should be taking notes. Yes, you absolutely should. Researchers have found that taking notes by hand is the most effective way to retain information. Those aiming to become full-time software developers will find this especially useful, as many interviews will require them to write code on a whiteboard.

Writing by hand can also help you plan your code before you move to the computer, once you start working on small projects and programs

The time you’ll save by figuring out which functions and classes you’ll need and how they’ll interact can be enormous.

 # Make your event more interactive!

When learning about Python data structures (strings, lists, and dictionaries), or when debugging an application, an interactive Python shell is one of your best learning tools. It’s easy to use, and it’s fun! We use it a lot on this site as well!! First, make sure that Python is installed on your computer before using the interactive Python shell (also known as a “Python REPL”).

If you want to learn how to do that, we’ve got a step-by-step tutorial.

python or python3 depending on your installation will activate the interactive Python shell.

For more specific directions, please refer to this link.

Using the shell as a learning tool is easy once you know how to launch it.

Read: Why Python is Better for Machine Learning and Artificial Intelligence?

#Take Breaks

When you are learning, it is important to take a step back and absorb the concepts that you are learning.

Work for 25 minutes, take a short break, and then repeat the process.

You must take breaks when studying, especially if you are learning a lot of new information. When debugging, breaks are especially important. Take a break if you hit a bug and can’t figure out what’s wrong. Think about taking a break from your computer, going for a walk, or talking on the phone with a friend. If you miss a quotation mark in your code, you’ll have to rewrite the entire program.

Fresh eyes can make a big difference in a situation.

# Initiate bug bounty hunting

In terms of bugs, it is inevitable that you will run into them once you start writing complex programs. All of us have experienced it. You shouldn’t let bugs get in your way of enjoying life. Imagine yourself as a bounty hunter.

As part of the process of debugging, it’s important to have a methodological approach. This can be done by going through your code in the order in which it is executed and checking that each part works. In your script, add the following line: import pdb; pdb.settrace() and run it. Here you’ll find the Python debugger. Python -m PDB can also be used to start the debugger from the command line.

#Create a collaborative environment with Make It!

Once things start to stick, you can accelerate your learning by collaborating with other professionals. You can make the most of working with others by following these strategies.

# Surround yourself with others who are also learning.

In spite of the fact that it may seem like coders work alone, it actually works best when they collaborate. The people you surround yourself with when learning how to code in Python are crucial to your success. Sharing tips and tricks is a great way to keep the conversation going. Not knowing anyone is not a problem. There are many ways to meet other Python enthusiasts! PythonistaCafe is a peer-to-peer learning community for Python enthusiasts.

#Teach.

Teaching something is considered to be one of the best methods of learning. The same applies to learning Python. Write blog posts explaining new concepts, record videos explaining something you learned, or simply talk to yourself at your computer. Chacune de cesstratégiesconsolideravotrecompréhension et feraressortir les lacunes.

# Pair Program

As part of pair programming, two developers collaborate at one computer to complete a task. “Driver” and “navigator,” respectively, are switched between the two developers. To get the most out of both sides, switch frequently. When you pair program, you not only have someone review your code, but you also get to see how someone else might approach a problem. When you return to coding on your own, being exposed to multiple ideas and ways of thinking will help you to solve problems.

# Contribute to Open Source Software

Open-source software is a model in which the source code is publicly available and anyone can contribute.

Open-source projects and open-source Python libraries are plentiful.

De plus, de nombreusesentreprisespublient des projets de libre accès.

In other words, you’ll be able to use code written and produced by the engineers at these companies.

Open-source Python projects are a great way to gain valuable experience.

In the case of a bug fix, you submit a “pull request” to have your fix patched into the source code.

You’ll then be reviewed by the project managers, who’ll provide feedback.

Learn the best practices for Python programming, and practice communicating with other developers, by participating in this workshop!

Go Ahead and Learn a Thing or Two!

These learning strategies will help you get started with Python.

Roadmap for Learning Python for Beginners – Real Python

In addition, we offer a Python course for beginners.

Coding is fun!

The post Tips to Learn Python Code Today (Tips That Actually Work) appeared first on .

In a world machine learning technology will going up and while going all manual tasks are being changing. Right now the machine learning algorithm helps for all industries such as healthcare, bank, software and Retail, Automotive, Government sector, Oil & Gas Industries.

The main feature of this revolution that stands out is how computing tools and techniques have been democratized. In the past 3 years, data scientists have made classy data-crunching machines by flawlessly executing advanced techniques.

Request Call Back

Top 10 algorithms every machine learning engineer need to know

There are 3 types of Machine Learning methods: Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

Here I am going to list the top 10 common Machine Learning Algorithms

1. Linear Regression

In Linear Regression we start the relationship between independent and dependent variables by fitting the best line. This best right line is known as regression line and represented by a linear equation Y= a *X + b.

In this equation:

Y – Dependent Variable

a – Slope

X – Independent variable

b – Intercept

These coefficients a and b are resulting based on reducing the sum of squared difference of distance between data points and regression line.

2. Logistic Regression

Logistic Regression, it forecasts the probability of occurrence of an event by fitting data to a logit function, and it is also known as logit regression.

Here I listed below are frequently used to help improve logistic regression models

• Include interaction terms
• Eliminate features
• Regularize techniques
• Use a non-linear model

3. Decision Tree

It is one different type of supervised learning algorithm that is typically used for classification problems. Unexpectedly, it works for both categorical and continuous dependent variables. In this algorithm, we divided the population into two or more similar sets.

4. SVM (Support Vector Machine)

In SVM, each data item as a point in n-dimensional space with the value of each feature being the value of a particular organization. These Lines called classifiers can be used to divide the data and plot them on a graph.

5. Naive Bayes

Naive Bayes is classifier accepts that the presence of a particular feature in a class is unconnected to the presence of any other different feature.

Compare to all Naive Bayesian is easy to form and useful for enormous datasets. And it is known to outperform even highly cultured classification methods.

6. kNN (k- Nearest Neighbors)

For this Knn can be used for both classification and regression. However, but most of the time for Knn using classification problems in the industry. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases by a popular vote of its k neighbors.

Before going to select KNN should consider below things

• KNN is computationally costlier

• Variables should be regularized, or else higher range variables can bias the algorithm

• Data still needs to be pre-processed.

7. K-Means

It is very simple and easy to classify a given data sets over a certain number of clusters. And it is a type of unsupervised algorithm. Data points inside a cluster are homogeneous and heterogeneous to noble groups.

How K-means forms clusters:

• The K-means algorithm choices k amount of points
• Each data point methods a cluster with the closest centroids
• It now makes new centroids based on the current cluster members.

With the above centroids, the closest distance for each data point is determined. This process is frequent until the centroids do not change.

8. Random Forest

Random Forest is a symbol term for an ensemble of decision trees. In Random Forest having a group of decision trees. To categorize a new object based on features, each tree gives a classification and we say the tree votes for that class.

Each tree is established & grown as follows:

• If the number of cases in the training set is N, then a sample of N cases is taken at random. This sample will be the training set for growing the tree.

• If there are M input variables, a no m <<M is stated such that at each node, m variables are selected at random out of the M, and the best divided on this m is used to divided the node. The value of m is thought endless during this process.

Each tree is grown to the most substantial extent possible. There is no pruning.

9. Dimensionality Reduction Algorithms

In today’s world, a massive number of data is being stored and analyzed by corporates and government sectors, research organizations. As a data scientist, you know that this raw data contains a lot of information the challenge is in classifying significant designs and variables.

10. Gradient Boosting Algorithms

Gradient is a boosting algorithm used when we deal with a lot of data to make an estimate with high estimate power. Improving is actually a collective of learning algorithms which combines the calculation of several base estimators in order to improve robustness over a single estimator.

If you want to start your career in machine learning? Then start now itself, this is the right time to start your career in Machine Learning. Because machine learning is trendy concepts so the field is increasing, and the sooner you understand the choice of machine learning tools, the rather you’ll be able to offer solutions to complex work problems. We are NearLearn, providing good knowledge of Python, Deep Learning with the Tensor flow, blockchain, react native and reactjs, machine learning training in Bangalore. If you have any queries regarding our training please contact www.nearlearn.com or info@nearlearn.com.

The post Advanced machine learning algorithms 2020 appeared first on .