Automation will mainly target the knowledge workers, who are highly paid and educated and involved in thinking and analytical jobs.
The robot revolution is anticipated for quite some time now and with the ongoing advancements in Machine Learning, Artificial Intelligence and Data Science, the future is near. However, it is also one of the most dreaded events for the workers going forward, who would be vulnerable to losing their respective jobs.
Going back to the 2017 McKinsey study, around 50% of the jobs in the manufacturing industries are automatable using the latest technology. However, according to the latest report, the white-collar workers, who are well-read and engaged in thinking and analytical jobs, are more likely to suffer the most.
According to a new study conducted by Michael Webb, Stanford University Economist, the powerful technologies of computer science like Artificial Intelligence and Machine Learning, which can make human-like decisions and grow using real-time data, will eventually target the white-collar workers. Artificial Intelligence has already made marked intrusions in the white-collar jobs, like telemarketing, which are primarily overseen by the bots. However, with the tireless efforts of the Data Scientists, along with the expansion of the Machine Learning course in India, it is believed to oust the majority of the knowledge workers, like chemical engineers, market researchers, market analysts, physicists, librarians and more.
The new research focuses on the intersecting subject-noun pairs in AI patents and job descriptions to find out the jobs that will be heavily affected by the Ai technology. For example, the job descriptions of market research analysts comprise of “data analysis”, “identifying markets” and “track market trends”, which are in fact, all covered by the AI patents that are existing. This new study looks far more progressive than the previous ones because it analyzes patents for the technology which are yet to develop completely.
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Statistics is a branch of mathematics which deals with the collection, analysis, interpretation and presentation of masses of numerical data. Statistics is a tool used to communicate our understanding of data. It helps us understand the world better, make assertions, and communicate our confidence in the statements we are making.
Two main statistical methods are used in data analysis:
Descriptive statistics: This method is used to summarize data from a sample using measures such as the mean or standard deviation
Inferential statistics: With this method, you can conclude data that are subject to random variation (e.g., observational errors, sampling variation).
This article is about the descriptive statistics which are used to describe and summarize the datasets. We are also going to see the available Python libraries to get those numerical quantities.
This whole topic will be covered in a series of two blogs. This first blog is about the types of measures in descriptive statistics. Furthermore, we will also see the built-in Python “Statistics” library, which has a relatively small number of the most important statistics functions.
Descriptive statistics can be defined as the measures that summarize a given data, and these measures can be broken down further into the measures of central tendency and the measures of dispersion. Measures of central tendency include mean, median, and the mode, while the measures of dispersion include standard deviation and variance.
We will cover the following topics in descriptive statistics:
The arithmetic mean is the sum of data divided by the number of data-points. It is a measure of the central location of data in a set of values that vary in range. In Python, we usually do this by dividing the sum of given numbers with the count of the number present. Python mean function can be used to calculate the mean/average of the given list of numbers. It returns the mean of the data set passed as parameters.
mean( ): Arithmetic mean (“average”) of data.
harmonic_mean( ): It is the reciprocal of the arithmetic mean of the reciprocals of the data (say for three numbers a, b and c, 1/mean = 3/(1/a + 1/b + 1/c)).
Median
median( ): Median or middle value of data is calculated as the mean of middle two. When the number of data points is odd, the middle data point is returned. The median is a robust measure of a central location and is less affected by the presence of outliers in your data compared to the mean.
median_low( ): Low median of data is calculated when the number of data points is odd. Here the middle value is usually returned. When it is even, the smaller of the two middle values is returned.
median_high( ): High median of data is calculated when the number of data points is odd. Here, the middle value is usually returned. When it is even, the larger of the two middle values is returned.
Mode
mode( ): Mode (most common value) of discrete data. The mode (when it exists) is the most typical value and is a robust measure of central location.
Measures of Dispersion
Measures of dispersion are statistics that describe how data varies, usually relative to the typical value. While measures of centre give us an idea of the typical value, measures of spread give us a sense of how much the data tends to diverge from the typical value.
These following functions (from the statistics module in python) calculate a measure of how much the population or sample tends to deviate from the typical or average values.
Population Variance
pvariance( ): Returns the population variance of data. Use this function to calculate the variance from the entire population. To estimate the variance from a sample, the variance ( ) function is usually a better choice. When called with the entire population, this gives the population variance σ². When called on a sample instead, this is the biased sample variance s², also known as variance with N degrees of freedom.
Population Standard Deviation
pstdev( ): Return the population standard deviation (the square root of the population variance)
Sample Variance
variance ( ): Returns the sample variance of data, an iterable of at least two real-valued numbers. Variance, or second moment about the mean, is a measure of the variability (spread or dispersion) of data. A large variance indicates that the data is spread out; a small variance indicates it is clustered closely around the mean. If the optional second argument is given to the function, it should be the mean of data. This is the sample variance s² with Bessel’s correction, also known as variance with N-1 degrees of freedom.
Sample Standard Deviation
stdev( ): Returns the sample standard deviation (the square root of the sample variance)
Conclusion
So, this article focuses on describing and summarizing the datasets, also helping you to calculate numerical quantities in Python. It’s possible to get descriptive statistics with pure Python code, but that’s rarely necessary. In the next series of this blog we will see the Python statistics libraries which are comprehensive, popular, and widely used especially for this purpose.
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Variable is the name given to the memory location where data is stored. Once a variable is stored, space is allocated in memory. Variables are named locations that are used to store references to the object stored in memory.
Now, coming to this article, here are some of the topics that will be covered in this article:
Rules to Define a Variable
Assigning Values to a Variable
Re-declaring a Variable in Python
Variable Scope
Deleting a Variable
Rules to Define a Variable
These are the few rules to define a python variable:
Python variable name can contain small case letters (a-z), upper case letters (A-Z), numbers (0-9), and underscore (_).
A variable name can’t start with a number.
We can’t use reserved keywords as a variable name.
The variable name can be of any length.
Python variable can’t contain only digits.
The variable names are case sensitive.
Assigning Values to a Variable
There is no need for an explicit declaration to reserve memory. The assignment is done using the equal to (=) operator.
Multiple Assignment in Python
Multiple variables can be assigned to the same variable.
Multi-value Assignment in Python
Multiple variables can be assigned to multiple objects.
Re-declaring a Variable in Python
After declaring a variable, one can again declare it and assign a new value to it. Python interpreter discards the old value and only considers the new value. The type of the new value can be different than the type of the old value.
Variable Scope
A variable scope defines the area of accessibility of the variable in the program. A Python variable has two scopes:
Local Scope
Global Scope
Python Local Variable
When a variable is defined inside a function or a class, then it’s accessible only inside it. They are called local variables and their scope is only limited to that function or class boundary.
If we try to access a local variable outside its scope, we get an error that the variable is not defined.
Python Global Variable
When the variable is not inside a function or a class, it’s accessible from anywhere in the program. These variables are called global variables.
Deleting a Variable
One can delete variable using the command “del”.
In the example below, the variable “d” is deleted by using command Del and when it is further proceeded to print, we get an error “variable name is not defined” which means the variable is already deleted.
Conclusion
In this article we have learned the concepts of Python variables which are used in every program. We also learned the rules associated to the naming of a variable, assigning value to a variable, scope of a variable and deleting a variable.
So, if you are also hooked into Python and looking for the best courses, Python course in Gurgaon is certainly a gem of a course!
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Before we get started with the process of building a Neural Network, we need to understand first what a Neural Network is.
A neural network is a collection of neurons connected by synapses. This collection is organized into three main layers: the input layer, the hidden layer, and the output layer.
In an artificial neural network, there are several inputs, which are called features, producing a single output, known as a label.
Analogy between Human Mind and Neural Network
Scientists believe that a living creature’s brain processes information through the use of a biological neural network. The human brain has as many as 100 trillion synapses – gaps between neurons – which form specific patterns when activated.
In the field of Deep Learning, a neural network is represented by a series of layers that work much like a living brain’s synapses. It is becoming a popular course now, with an array of career opportunities. Thus, Deep learning Certification in Gurgaon is a must for everyone.
Scientists use neural networks to teach computers how to do things for themselves. The whole concept of Neural network and its varied applications are pretty interesting. Moreover, with the matchless Neural Networks Training in Delhi, you need not look any further.
There are numerous kinds of deep learning and neural networks:
Feedforward Neural Network – Artificial Neuron
Radial basis function Neural Network
Kohonen Self Organizing Neural Network
Recurrent Neural Network (RNN) – Long Short Term Memory
Convolutional Neural Network
Modular Neural Network
Generative adversarial networks (GANs)
Working of a Simple Feedforward Neural Network
It takes inputs as a matrix (2D array of numbers).
Multiplies the input by a set weight (performs a dot product aka matrix multiplication).
Applies an activation function.
Returns an output.
Error is calculated by taking the difference from the desired output from the data and the predicted output. This creates our gradient descent, which we can use to alter the weights.
The weights are then altered slightly according to the error.
To train, this process is repeated 1,000+ times. The more the data is trained upon, the more accurate our outputs will be.
Implementation of a Neural Network with Python and Keras
Keras has two types of models:
Sequential model
The model class used with functional API
Sequential model is probably the most used feature of Keras. Primarily, it represents the array of Keras Layers. It is convenient and builds different types of Neural Networks really quick, just by adding layers to it. Keras also has different types of Layers like Dense Layers, Convolutional Layers, Pooling Layers, Activation Layers, Dropout Layers etc.
The most basic layer is Dense Layer. It has many options for setting the inputs, activation function and so on. So, let’s see how one can build a Neural Network using Sequential and Dense.
First, let’s import the necessary code from Keras:
After this step, the model is ready for compilation. The compilation step asks to define the loss function and the kind of optimizer which should be used. These options depend on the problem one is trying to solve.
Now, the model is ready to get trained. Thus, the parameters get tuned to provide the correct outputs for a given input. This can be done by feeding inputs at the input layer and then, getting an output.
After this one can calculate the loss function using the output and use backpropagation to tune the model parameters. This will fit the model parameters to the data.
Output of the above cell:-
This output shows the loss decrease and the accuracy increase over time. At this point, one can experiment with the hyper-parameters and neural network architecture to get the best accuracy.
After getting the final model architecture, one can now take the model and use feed-forward passes and predict inputs. To start making predictions, one can use the testing dataset in the model that has been created previously. Keras enables one to make predictions by using the .predict() function.
Some points to be remembered while building a strong Neural Network
1. Adding Regularization to Fight Over-Fitting
The predictive models mentioned above are prone to a problem of overfitting. This is a scenario whereby the model memorizes the results in the training set and isn’t able to generalize on data that it hasn’t seen.
In neural networks, regularization is the technique that fights overfitting by adding a layer in the neural network. It can be done in 3 ways:
L1 Regularization
L2 Regularization
Dropout Regularization
Out of these, Dropout is a commonly used regularization technique. In every iteration, it adds a Dropout layer in the neural network and thereby, deactivates some neurons. The process of deactivating neurons is usually random.
2. Hyperparameter Tuning
Grid search is a technique that you can use to experiment with different model parameters to obtain the ones that give you the best accuracy. This is done by trying different parameters and returning those that give the best results. It helps in improving model accuracy.
Conclusion
Neural Network is coping with the fast pace of the technology of the age remarkably well and thereby, inducing the necessity of courses like Neural Network Machine Learning Python, Neural Networks in Python courseand more. Though these advanced technologies are just at their nascent stage, they are promising enough to lead the way to the future.
In this article, Building and Training our Neural Network is shown. This simple Neural Network can be extended to Convolutional Neural Network and Recurrent Neural Network for more advanced applications in Computer Vision and Natural Language Processing respectively.
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Python is an extremely readable and versatile high-level programming language. It supports both Object-oriented programming as well as Functional programming. It is generally referred to as an interpreted language which means that each line of code is executed one by one and if the interpreter finds an error it stops proceeding further and gives an error message to the user. This makes Python a widely regarded language, fueling Machine Learning Using Python, Text Mining with Python course and more. Furthermore, with such a high-end programming language, Python for data analysis looks ahead for a bright future.
In the Structure of Python
Computer languages have a structure just like human languages. Therefore, even in Python, we have comments, variables, literals, operators, delimiters, and keywords.
To understand the program structure of Python we will look at the following in this article: –
Python Statement
Simple Statement
Compound Statement
Multiple Statements Per Line
Line Continuation
Implicit Line Continuation
Explicit Line Continuation
Comments
Whitespace
Indentation
Conclusion
Python Statement
A statement in Python is a logical instruction that the interpreter reads and executes. The interpreter executes statements sequentially, one by one. In Python, it could be an assignment statement or an expression. The statements are mostly written in such a style so that each statement occupies a single line.
Simple Statements
A simple statement is one that contains no other statements. Therefore, it lies entirely within a logical line. An assignment is a simple statement that assigns values to variables, unlike in some other languages; an assignment in Python is a statement and can never be part of an expression.
Compound Statement
A compound statement contains one or more other statements and controls their execution. A compound statement has one or more clauses, aligned at the same indentation. Each clause has a header starting with a keyword and ending with a colon (:), followed by a body, which is a sequence of one or more statements. When the body contains multiple statements, also known as blocks, these statements should be placed on separate logical lines after the header line, indented four spaces rightward.
Multiple Statements per Line
Although it is not considered good practice multiple statements can be written in a single line in Python. It is advisable to avoid multiple statements in a single line. But, if it is necessary, then it can be written with the help of semicolon (;) as the terminator of every statement.
Line Continuation
In Python there might be some cases when a single statement is too long that does not fit the browser window and one needs to scroll the screen left or right. This can be a case of assignment statement with many terms or defining a lengthy nested list. These long statements of code are generally considered a poor practice.
To maintain readability, it is advisable to split the long statement into parts across several lines. In Python code, a statement can be continued from one line to the next in two different ways: implicit and explicit line continuation.
Implicit Line Continuation
This is the more straightforward technique for line continuation. In implicit line continuation, one can split a statement using either of parentheses ( ), brackets [ ] and braces { }. Here, one needs to enclose the target statement using the mentioned construct.
Explicit Line Continuation
In cases where implicit line continuation is not readily available or practicable, there is another option. This is referred to as an explicit line continuation or explicit line joining. Here, one can right away use the line continuation character (\) to split a statement into multiple lines.
Comments
A comment is text that doesn’t affect the outcome of a code; it is just a piece of text to let someone know what you have done in a program or what is being done in a block of code. This is especially helpful when a code is written and someone is analyzing it for bug fixing or making a change in logic, by reading a comment one can understand the purpose of code much faster than by just going through the actual code.
There are two types of comments in Python. 1. Single line comment 2. Multiple line comment
Single line comment
In python, one can use # special character to start the comment.
Multi-line comment
To have a multi-line comment in Python, one can use Triple Double Quotation at the beginning and the end of the comment.
Whitespace
One can improve the readability of the code with the use of whitespaces. Whitespaces are necessary for separating the keywords from the variables or other keywords. Whitespace is mostly ignored by the Python interpreter.
Indentation
Most of the programming languages provide indentation for better code formatting and don’t enforce to have it. However, in Python, it is mandatory to obey the indentation rules. Typically, we indent each line by four spaces (or by the same amount) in a block of code. Also for creating compound statements, the indentation will be of utmost necessity.
Conclusion
So, this article was all about how to structure the Python program. Here, one can learn what constitutes a valid Python statement and how to use implicit and explicit line continuation to write a statement that spans multiple lines. Furthermore, one can also learn about commenting Python code, and about the use of whitespace and indentation to enhance the overall readability.
We hope this article was helpful to y ou. If you are interested in similar blogs, stay glued to our website, and keep following all the news and updates from Dexlab Analytics.
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Alteryx Analytics is primarily looking to ease the usability of the platform in all of the updates that are yet to come. The esteemed data analytics platform is concentrating on reducing the complexities to attract more users and thus, widen their age-old user base beyond that of the data scientists and data analytics professionals.
Alteryx is headquartered in Irvine, California. It was founded as SRC LLC in 1997 and comes with a suite of four tools to help the world of data scientists and data analysts to manage and interpret data easily. Alteryx Connect, Alteryx Designer, Alteryx Promote and Alteryx Server are the main components of the analytics platform of Alteryx. Thus, it is worth mentioning that the Alteryx Certification Courseis a mustif you are looking to make a career out of data science/data analytics.
A Quick Glance at the Recent Updates
The reputed firm launched a recent version of Alteryx 2019.3, in October, and is likely to release the Alteryx 2019.4 as a successor to it. The latter is scheduled for a December release.
What’s in the Update?
Talking about the all-new version Alteryx 2019.3, Ashley Kramer, senior vice president of product management at Alteryx, said that the latest version promises 25 new and upgraded features, all of them focussing on the user-friendliness of the platform at large.
One of the prominent features of the new version is a significant decrease in the total number of clicks that a user will take to arrive at the option of visualizing data to make analytic decisions.
Data profiling helps the users to visualize the data while they are working with it. Here, Alteryx discovered a painless way to work with data by modeling the bottom of the screen in a format similar to that of MS Excel.
All of these changes and additions are done keeping in mind the features that the “customers had been asking for,” according to Kramer.
Now, with the December update, which will come with an enhanced mapping tool, the Alteryx analytics will strive to further lower the difficulties surrounding the platform.
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Python is an extremely readable and versatile high-level programming language. Many companies such as Google, YouTube, Dropbox use the language for developing applications. It also finds its use extensively in diverse fields as in Python for data analysis, Machine Learning Using Python, Natural Language Processing, Web Development, Scientific Computing, Image processing, Robotics, Computer Vision and many more.
It supports both Object-oriented programming and Functional programming. Python is generally referred to as an interpreted language which implies that each line of code is executed one by one and if the interpreter finds an error, it stops immediately with an error message on the screen.
Another important feature of Python is its interactive prompt. A Python statement can be typed and immediately executed, which is in sharp contradiction to any other compiled language.
What are Python 2.x and Python 3.x?
There are two main versions of Python: Python 2.x and Python 3.x. If someone is new to Python, then he/she might be in confusion about which version to use. However, in the current scenario, we can easily migrate from Python 2 to Python 3, as the Python Software Foundation has finally taken the step to formally announce that Python 2 will reach the end of life (EOL) on January 1st, 2020.
Key differences between Python 2.x and Python 3.x
This article discusses the differences between these two versions of Python, making Python 3 less confusing for a new programmer.
Print Function
In Python 2, print is a statement. There is no need of parenthesis.
In Python 3, print is a function. It needs parenthesis.
Integer Division
In Python 2, if the division operator is performed on two integers, then the output will be an integer for example: – 7/3 = 2.
In Python 3, if the division operator is performed on two integers, then the output will be accurate. It can also be in float for example: – 7/3 = 2.33.
To get the result in an integer only a different division operator is used that is (//)it returns an integer result for example, – 7//3 = 2.
3. Unicode Support
Both the versions of Python can handle strings (sequences of characters) differently.
Python 2 uses the ASCII encoding standard by default. ASCII is limited to representing 256 characters. This limits the flexibility of Python to encode the characters, particularly non-standard ones. Using Unicode in Python 2 requires extra syntax—for example when using print, the input text is to be wrapped in the Unicode() function to handle special characters.
In Python 3, Unicode is the default. The Unicode standard is much more versatile—it supports over 128,000 characters. There is no need for an extra syntax to define the Unicode values—they get printed automatically as utf-8 strings.
Range Function
In Python 2, the range function returns a list of numbers.
In Python 2, the xrange class represents an iterable that provides the same object.
In Python 3, original range function is removed and xrange is renamed to range:
In Python 3, it is needed to convert the range object to a list if someone desires the same result as the range function provides in Python 2.
Input() Method
Mainly what is expected from the input() method is that it reads input as string, then it can be converted into any datatype as per the requirement.
In Python 2, it has both the input() and raw_input() methods for taking input. The difference between the raw_input() and input()is that the raw_input() reads input as a string while the input() reads input as string only if it is inside quotes else reads as an integer.
In Python 3, there is no raw_input() method. The raw_input() method is replaced by input() in python 3.
If someone still wants to use the input() method like in python 2, then it can be availed by using eval() method.
There are many other differences between Python 2 and Python 3 like: –
Next() Method
In Python 2, .next() method is used and in Python 3 next() function is used to iterate the next element of an iterator.
Raising Exception
To raise an exception in Python 3, the argument should be in parenthesis, while in Python 2, it is not necessary.
Handling Exception
Handling exception is also changed in Python 3, “as” keyword is used in Python 3, while it is not necessary in Python 2.
So, if someone is a beginner, then it is strongly recommended to use Python 3 because it is the future of Python and also January 1, 2020, will be the last day of Python 2. It means that no improvement will be done anymore after that day, even if someone finds a security problem in it.
It is highly recommended to upgrade the version of the programming language to Python 3. Some ways can help the Python 2 users in porting their code from Python 2 to Python 3 and get the feel of Python 3 and figure out how it is different from Python 2. The code can be imported by using tools like “Futurize” and “Modernize”. Also, if someone wants to check the availability of Python 3 as part of his tests, then “caniusepython3.check()” can be used.
As a final note, everyone must look for upgrading their Python version to Python 3 to understand the subtleties of the new version and usher in the future. However, if you are interested in Deep learning for computer vision with Python and similar courses, then opt for the premium Python training institute in Delhi now!
AI plays a crucial role in our everyday lives. By now, we are aware of AI’s glaring significance in our very existence. Nevertheless, you would be surprised to know that AI has already imbibed some of the skills that we, humans, possess. Ahead, we’ve 8 incredible skills that AI has learnt over the years:
Read
Wondering how to summarize all those kilobytes of information? AI-powered SummarizeBot is the answer. Whether its books, news articles, weblinks, audio/image files or legal documents, ATS (automatic text summarization) reads everything and records the important information. Natural Language Processing (NLP), artificial intelligence, machine learning and blockchain technologies are in play here.
Write
Did you know that myriad news enterprises and seasoned journalists rely on AI to write? The New York Times, Reuters, Washington Post and more have turned to artificial intelligence to craft interesting reading pieces. Also, AI is expected to enhance the process of creative writing as well. Even, it has generated a novel that was shortlisted for a prestigious award.
See
Machine vision is in the hype. It is implemented in different ways in today’s world, such as facilitating self-driven cars, facial recognition for payment portals, police work and more. The main concept of machine vision is to let the computers ‘visualize’ the world, analyze key data and make decisions thereafter.
Speak
We are fortunate enough to have Google Maps and Alexa to give us directions and respond to our queries but Google Duplex takes it to a whole new level, courtesy AI. With the help of this robust technology, Duplex can schedule appointments and finish tasks on phone in a very interactive language. It can also respond perfectly to human behaviors.
Hear and Understand
Detecting gunshots and alerting to-the-purpose agencies is one of the greatest things achieved by AI. It means AI can hear and understand sound. It is very well evident in how digital voice assistants respond to your queries regarding weather or a day’s agenda. Working professionals simply love the efficiency, accuracy and convenience of automated meeting minutes provided by AI.
Touch
With the help of cameras and sensors, a robot can identify and handpick ‘supermarket ripe’ blueberries and put them in your basket. The creator of the robot even asserts that it is designed to pick one blueberry every 10 seconds for 24 hours a day!
Smell
A team of AI researchers are at present developing robust AI models that can detect illnesses – simply by smelling. The model is designed in such a way so that it can notice chemicals, known as aldehydes that cause human stress and diseases, including diabetes, cancer and brain injuries. AI bots can even identify other caustic chemicals or gas leaks. Of late, IBM is using AI to formulate new perfumes.
Perceive Emotions
Today, AI tools can observe human emotions and track them down as one watches videos. Artificial emotional intelligence can collect meaningful data from a person’s facial expressions or body language, analyze it to determine what emotion he/she is likely to express and then ascertain an action base on that detail.
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Machine Learning surely needs no introduction. Joining forces with Data Science and Big Data, Machine Learning is one of the principal technologies, which is carving the future for us. From self-propelled cars to voice assistants, to surgical robots, Artificial Intelligence is already amongst us.
Besides, with this cutting-edge technology, marketing is also witnessing a fresh bloom, irrespective of the field you are working on. Thus, it is obvious that the career opportunities have quickly and radically shifted in the way of the candidates who are well-versed with Machine Learning platforms and languages. If you are also looking forward to shooting your career up, the premium Machine Learning course in Indiais the place you should reach now!
Learning Machine Learning is No More a Pain Now!
Whether you are a professional or a fresher planning your way to be successful as a Machine Learning professional, you must ensure that you are updated. Besides, you should also be careful that you have certain skills in your grip that you can work on!
However, if you are not aware of them still, here are the skills that you need to focus on to rest assured:
Programming Languages
As you speak English and/or your regional languages accepted to your society in order to communicate comprehensibly, you also need to be well-versed with the languages specific to Machine Learning.
If you believe that you can already boast of considerable knowledge of R & Python, then you shall extend your knowledge a bit more towards the advanced methods of analysis. Brief know how of the coding structures, Data Modeling and Data Visualization will help you steer your career forwards.
Statistics and Probability
If you are seeking to make a career out of Machine Learning, it is important to note that you should have a good grip of statistics and probability. Now, with the thorough courses of Python for Data Analysis along with extensive knowledge of statistics and probability from Dexlab Analytics,it will be easier than ever.
Besides all these, you also need to grasp significant insights into the improved algorithms and clustering methods.
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