Principal Component Analysis or PCA is one of the simplest and fundamental techniques used in machine learning. It is perhaps one of the oldest techniques available for dimensionality reduction, and thus, its understanding is of paramount importance for any aspiring Data Scientist/Analyst. An in-depth understanding of PCA in R will not only help in the implementation of effective dimensionality reduction but also help to build the foundation for development and understanding of other advanced and modern techniques.
PCA aims to achieve two
Real-life data has several features generated from numerous resources. However, our machine learning algorithms are not adept enough to handle high dimensions efficiently. Feeding several features, all at once, almost always leads to poor results since the models cannot grasp and learn from such volume altogether. This is called the “Curse of Dimensionality” which leads to unsatisfactory results from the models implemented. Principal Component Analysis in R helps resolve this problem by projecting n dimensions to n-x dimensions (where x is a positive number), preserving as much variance as possible. In other words, PCA in R reduces the number of features by transforming the features into a lesser number of projections of themselves.
Our visualization systems are limited to 2-dimensional space which prevents us from forming a visual idea of the high dimensional features in the dataset. PCA in R resolves this problem by projecting n dimensions to a 2-D environment, enabling sound visualization. These visualizations sometimes reveal a great deal about the data. For instance, the new feature projections may form clusters in the 2-D space which was previously not perceivable in higher dimensions.
Principal Component Analysis in R works with the simple idea of projection of a higher space to a lower space or dimension
The two alternate objectives of Principal Component Analysis are:
1. Variance Maximization
2. Distance Minimization
Let us demonstrate the above with the help of simple examples. If you have 2 features, and you wish to reduce the features to a 1-D feature set using PCA in R, you must lookout for the direction with maximal spread/variance. This becomes the new direction on which every data point is projected. The direction perpendicular to this direction has the least variance, and is thus, discarded.
Alternately, if one focuses on the perpendicular distance between a data point and the direction of maximum variance, our objective shifts to the minimization of that distance. This is because, lesser the distance, higher is the authenticity of the projection.
On completion of these projections, you would have successfully transformed your 2-D data to a 1-D dataset.
Principal Component Analysis in R locates the distance of maximal spread (or direction of minimal distance from data points) with the use of Eigen Vectors and Eigen Values. Every Eigen Vector (Vi) corresponds to an Eigen Value (Ei).
If X is a feature matrix (matrix with the feature values),
covariance matrix S = XT. X
If EiVi = SVi ,
Then Ei is an Eigen Value, and Vi becomes the corresponding Vector.
If there are d dimensions, there will be d Eigenvalues with d corresponding Eigen Vectors, such that:
Each corresponding to V1, V2, V3, …., Vd
Here the vector corresponding to the largest Eigenvalue is the direction of Maximal spread since rotation occurs such that V1 is aligned with maximal variance in the feature space. Vd here has the least variance in its direction.
A very interesting property of Eigenvectors is the fact that if any two vectors are picked randomly from the set of d vectors, they will turn out to be perpendicular to each other. This happens because they align themselves such that they catch the most opposing directions in terms of variance.
When deciding between two Eigen Vector directions, Eigenvalues come into play. If V1 and V2 are two Eigen Vectors (perpendicular to each other), the values associated with these vectors, E1 and E2, help us identify the “percentage of variance explained” in either direction.
Percentage of variance explained Ei/(Sum(d Eigen Values)) where i is the direction we wish to calculate the percentage of variance explained for.
Principal Component Analysis in R can either be applied with manual code using the above mathematical intuition, or it can be done using R’s inbuilt functions.
Even if the mathematical concept failed to leave a lasting impression on your mind, be assured that it is not of great consequence. On the other hand, understanding the basic high-level intuition counts. Without using the mathematical formulas, PCA in R can be easily applied using R’s prcomp() and princomp() functions which can be found here.
In order to demonstrate Principal Component Analysis, we will be using R, one of the most widely used languages in Data Science and Machine Learning. R was initially developed as a tool to aid researchers and scientists dealing with statistical problems in the academic field. With time, as more individuals from the academic spheres started seeping into the corporate and industrial sectors, they brought along R and its phenomenal uses along with them. As R got integrated into the IT sector, its popularity increased manifold and several revisions were made with the release of every new version. Today R has several packages and integrated libraries which enables developers and data scientists to instantly access statistical solutions without having to go into the complicated details of the operations. Principal Component Analysis is one such statistical approach which has been taken care of very well by R and its libraries.
For demonstrating PCA in R, we will be using the Breast Cancer Wisconsin Dataset which can be downloaded from here: Data Link
These code statements help to read data into the variables wdbc.
wdbc.pr <- prcomp(wdbc[c(3:32)], center = TRUE, scale = TRUE) summary(wdbc.pr)
The prcomp() function helps to apply PCA in R on the data variable wdbc. This function of R makes the entire process of implementing PCA as simple as writing just one line of code. The internal operations and functions are taken care of and are even optimized in terms of memory and performance to carry out the operations optimally. The range 3:32 is used to tell the function to apply PCA only on the features or columns which lie in the range of 3 to 32. This excludes the sample ID and diagnosis variables since they are identification columns and are invalid as features with no direct significance with regard to the target variable.
now stores the values of the principal components.
Let us now
visualize the different attributes of the resulting Principal Components for
the 30 features:
screeplot(wdbc.pr,type="l",npcs=15,main="Screeplot of the first 10 PCs")<br>
clearly demonstrates that the first 6 components account for 90% of the variance
in the dataset (with Eigen Value > 1). This means that one can easily
exclude 24 features out of 30 features in order to preserve 90% of the data.
Limitations of PCA
Even though Principal Component Analysis in R displays a highly intuitive technique, it hosts certain shocking limitations.
1. Loss of Variance: If the percentage of variance against the chosen axis is around 50-60%, it is evident that 40-50% of the information which contributes to the variance of the dataset is lost during dimensionality reduction. This happens often when the data is spherical or bulging in nature.
2. Loss of Clusters: If there are several clusters present in the original dataset, but most of them lie in the direction perpendicular to the chosen direction. Thus, all the points from different clusters will be projected to the same region on the line of chosen direction, leading to one cluster of data points which are in fact quite different in nature.
3. Loss of Data Patterns: If the dataset forms a nice wavy pattern in direction of maximal spread, PCA takes to project all the points on the line aligned against the direction. Thus, data points which formed a wave function are concentrated on one-dimensional space.
These demonstrate how PCA in R, even though very effective for certain datasets, is a weak instrument for dimensionality reduction or visualization. To resolve these limitations to a certain extent, t-SNE, which is another dimensionality reduction algorithm, is used. Stay tuned to our blogs for a similar and well-guided walkthrough in t-SNE.
Visualizing the data is important as it makes it easier to understand large amount of complex data using charts and graphs than studying documents and reports. It helps the decision makers to grasp difficult concepts, identify new patterns and get a daily or intra-daily view of their performance. Due to the benefits it possess, and the rapid growth in analytics industry, businesses are increasingly using data visualizations; which can be assessed from the prediction that the data visualization market is expected to grow annually by 9.47% to $7.76 billion by 2023 from $4.51 billion in 2017.
R is a programming language and a software environment for statistical computing and graphics. It offers inbuilt functions and libraries to present data in the form of visualizations. It excels in both basic and advanced visualizations using minimum coding and produces high quality graphs on large datasets.
This article will demonstrate the use of its packages ggplot2 and plotly to create visualizations such as scatter plot, boxplot, histogram, line graphs, 3D plots and Maps.
#install package ggplot2
#load the package
There are a lot of datasets available in R in package ‘datasets’, you can run the command data() to list those datasets and use any dataset to work upon. Here I have used the dataset named ‘economics’ which gives the monthly U.S. data of various economic variables like unemployment for the time period 1967-2015.
You can view the data using view function-
We’ll make a simple scatter plot to view how unemployment has fluctuated over the years by using plot function-
ggplot() is used to initialize the ggplot object which can be used to declare the input dataframe and set of plot aesthetics. We can add geom components to it that acts as its layer and are used to specify the plot’s features.
We would use its feature geom point which is used to create scatter plots.
When there is overplotting, one or more points are in the same place and we can’t tell by looking at the plot that how many points are there. In that case, we can use the jitter geom which adds a small amount of variation to the location of each point that is it slightly moves the point, which is used to spread out the points that would otherwise be overplotted.
+labs(title="Number of unemployed people in U.S.A. from 1967 to 2015",
x="Year",y="Number of unemployed people")
Let’s group the data according to year and view how average unemployment fluctuated through these years.
We will load dplyr package to manipulate our data and lubridate package to work with date column.
Now we will use mutate function to create a column year from the date column given in economics dataset by using the year function of lubridate package. And then we will group the data according to year and summarise it according to average unemployment-
Now, lets view the data as a line plot using line geom of ggplot2
(Since here we want the height of the bar be equal to avg_unempl, so we need to specify stat equal to identity)
Plotting Time Series Data
In this section, I’ll be using a dataset that records the number of tourists who visited India from 2001 to 2015 which I have rearranged such that it has 3 columns, country, year and number of tourists arrived.
To visualize the plot of the number of tourists that visited the countries over the years in the form of line graph, we use geom_line-
For convenience purpose, you can change the theme of the background as well, here I am keeping the theme as white-
These were some basic functions of ggplot2, for more functions, check out the official guide.
Plotly is deemed to be one of the best data visualization tools in the industry.
Lets construct a simple line graph of two vectors by using plot_ly function that initiates a visualization in plotly. Since we are creating a line graph, we have to specify type as ‘scatter’ and mode as ‘lines’.
We can modify the map as well. Here we have increased the size of the points and changed its color. We have also added text that is the location of the point which would show the location name when the cursor is placed on it.
These were some of the visualizations from package ggplot2 and plotly. R has various other packages for visualizations like graphics and lattice. Refer to the official documentation of R to know more about these packages.
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There are a huge number of ML algorithms out there. Trying to classify them leads to the distinction being made in types of the training procedure, applications, the latest advances, and some of the standard algorithms used by ML scientists in their daily work. There is a lot to cover, and we shall proceed as given in the following listing:
1. Statistical Algorithms
Statistics is necessary for every machine learning expert. Hypothesis testing and confidence intervals are some of the many statistical concepts to know if you are a data scientist. Here, we consider here the phenomenon of overfitting. Basically, overfitting occurs when an ML model learns so many features of the training data set that the generalization capacity of the model on the test set takes a toss. The tradeoff between performance and overfitting is well illustrated by the following illustration:
Overfitting – from Wikipedia
Here, the black curve represents the performance of a classifier that has appropriately classified the dataset into two categories. Obviously, training the classifier was stopped at the right time in this instance. The green curve indicates what happens when we allow the training of the classifier to ‘overlearn the features’ in the training set. What happens is that we get an accuracy of 100%, but we lose out on performance on the test set because the test set will have a feature boundary that is usually similar but definitely not the same as the training set. This will result in a high error level when the classifier for the green curve is presented with new data. How can we prevent this?
Cross-Validation is the killer technique used to avoid overfitting. How does it work? A visual representation of the k-fold cross-validation process is given below:
The entire dataset is split into equal subsets and the model is trained on all possible combinations of training and testing subsets that are possible as shown in the image above. Finally, the average of all the models is combined. The advantage of this is that this method eliminates sampling error, prevents overfitting, and accounts for bias. There are further variations of cross-validation like non-exhaustive cross-validation and nested k-fold cross validation (shown above). For more on cross-validation, visit the following link.
There are many more statistical algorithms that a data scientist has to know. Some examples include the chi-squared test, the Student’s t-test, how to calculate confidence intervals, how to interpret p-values, advanced probability theory, and many more. For more, please visit the excellent article given below:
Classification refers to the process of categorizing data input as a member of a target class. An example could be that we can classify customers into low-income, medium-income, and high-income depending upon their spending activity over a financial year. This knowledge can help us tailor the ads shown to them accurately when they come online and maximises the chance of a conversion or a sale. There are various types of classification like binary classification, multi-class classification, and various other variants. It is perhaps the most well known and most common of all data science algorithm categories. The algorithms that can be used for classification include:
Support Vector Machines
Linear Discriminant Analysis
and many more. A short illustration of a binary classification visualization is given below:
For more information on classification algorithms, refer to the following excellent links:
Regression is similar to classification, and many algorithms used are similar (e.g. random forests). The difference is that while classification categorizes a data point, regression predicts a continuous real-number value. So classification works with classes while regression works with real numbers. And yes – many algorithms can be used for both classification and regression. Hence the presence of logistic regression in both lists. Some of the common algorithms used for regression are
Support Vector Regression
Partial Least-Squares Regression
For more on regression, I suggest that you visit the following link for an excellent article:
Both articles have a remarkably clear discussion of the statistical theory that you need to know to understand regression and apply it to non-linear problems. They also have source code in Python and R that you can use.
Clustering is an unsupervised learning algorithm category that divides the data set into groups depending upon common characteristics or common properties. A good example would be grouping the data set instances into categories automatically, the process being used would be any of several algorithms that we shall soon list. For this reason, clustering is sometimes known as automatic classification. It is also a critical part of exploratory data analysis (EDA). Some of the algorithms commonly used for clustering are:
Hierarchical Clustering – Agglomerative
Hierarchical Clustering – Divisive
K-Nearest Neighbours Clustering
EM (Expectation Maximization) Clustering
Principal Components Analysis Clustering (PCA)
An example of a common clustering problem visualization is given below:
The above visualization clearly contains three clusters.
Another excellent article on clustering refer the link
Dimensionality Reduction is an extremely important tool that should be completely clear and lucid for any serious data scientist. Dimensionality Reduction is also referred to as feature selection or feature extraction. This means that the principal variables of the data set that contains the highest covariance with the output data are extracted and the features/variables that are not important are ignored. It is an essential part of EDA (Exploratory Data Analysis) and is nearly always used in every moderately or highly difficult problem. The advantages of dimensionality reduction are (from Wikipedia):
It reduces the time and storage space required.
Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model.
It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D.
It avoids the curse of dimensionality.
The most commonly used algorithm for dimensionality reduction is Principal Components Analysis or PCA. While this is a linear model, it can be converted to a non-linear model through a kernel trick similar to that used in a Support Vector Machine, in which case the technique is known as Kernel PCA. Thus, the algorithms commonly used are:
Ensembling means combining multiple ML learners together into one pipeline so that the combination of all the weak learners makes an ML application with higher accuracy than each learner taken separately. Intuitively, this makes sense, since the disadvantages of using one model would be offset by combining it with another model that does not suffer from this disadvantage. There are various algorithms used in ensembling machine learning models. The three common techniques usually employed in practice are:
Simple/Weighted Average/Voting: Simplest one, just takes the vote of models in Classification and average in Regression.
Bagging: We train models (same algorithm) in parallel for random sub-samples of data-set with replacement. Eventually, take an average/vote of obtained results.
Boosting: In this models are trained sequentially, where (n)th model uses the output of (n-1)th model and works on the limitation of the previous model, the process stops when result stops improving.
Stacking: We combine two or more than two models using another machine learning algorithm.
(from Amardeep Chauhan on Medium.com)
In all four cases, the combination of the different models ends up having the better performance that one single learner. One particular ensembling technique that has done extremely well on data science competitions on Kaggle is the GBRT model or the Gradient Boosted Regression Tree model.
We include the source code from the scikit-learn module for Gradient Boosted Regression Trees since this is one of the most popular ML models which can be used in competitions like Kaggle, HackerRank, and TopCoder.
In the last decade, there has been a renaissance of sorts within the Machine Learning community worldwide. Since 2002, neural networks research had struck a dead end as the networks of layers would get stuck in local minima in the non-linear hyperspace of the energy landscape of a three layer network. Many thought that neural networks had outlived their usefulness. However, starting with Geoffrey Hinton in 2006, researchers found that adding multiple layers of neurons to a neural network created an energy landscape of such high dimensionality that local minima were statistically shown to be extremely unlikely to occur in practice. Today, in 2019, more than a decade of innovation later, this method of adding addition hidden layers of neurons to a neural network is the classical practice of the field known as deep learning.
Deep Learning has truly taken the computing world by storm and has been applied to nearly every field of computation, with great success. Now with advances in Computer Vision, Image Processing, Reinforcement Learning, and Evolutionary Computation, we have marvellous feats of technology like self-driving cars and self-learning expert systems that perform enormously complex tasks like playing the game of Go (not to be confused with the Go programming language). The main reason these feats are possible is the success of deep learning and reinforcement learning (more on the latter given in the next section below). Some of the important algorithms and applications that data scientists have to be aware of in deep learning are:
Long Short term Memories (LSTMs) for Natural Language Processing
Recurrent Neural Networks (RNNs) for Speech Recognition
Convolutional Neural Networks (CNNs) for Image Processing
Deep Neural Networks (DNNs) for Image Recognition and Classification
Hybrid Architectures for Recommender Systems
Autoencoders (ANNs) for Bioinformatics, Wearables, and Healthcare
Deep Learning Networks typically have millions of neurons and hundreds of millions of connections between neurons. Training such networks is such a computationally intensive task that now companies are turning to the 1) Cloud Computing Systems and 2) Graphical Processing Unit (GPU) Parallel High-Performance Processing Systems for their computational needs. It is now common to find hundreds of GPUs operating in parallel to train ridiculously high dimensional neural networks for amazing applications like dreaming during sleep and computer artistry and artistic creativity pleasing to our aesthetic senses.
Artistic Image Created By A Deep Learning Network. From blog.kadenze.com.
For more on Deep Learning, please visit the following links:
In the recent past and the last three years in particular, reinforcement learning has become remarkably famous for a number of achievements in cognition that were earlier thought to be limited to humans. Basically put, reinforcement learning deals with the ability of a computer to teach itself. We have the idea of a reward vs. penalty approach. The computer is given a scenario and ‘rewarded’ with points for correct behaviour and ‘penalties’ are imposed for wrong behaviour. The computer is provided with a problem formulated as a Markov Decision Process, or MDP. Some basic types of Reinforcement Learning algorithms to be aware of are (some extracts from Wikipedia):
Q-Learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation “model-free”) of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state. Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy. “Q” names the function that returns the reward used to provide the reinforcement and can be said to stand for the “quality” of an action taken in a given state.
State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy. This name simply reflects the fact that the main function for updating the Q-value depends on the current state of the agent “S1“, the action the agent chooses “A1“, the reward “R” the agent gets for choosing this action, the state “S2” that the agent enters after taking that action, and finally the next action “A2” the agent choose in its new state. The acronym for the quintuple (st, at, rt, st+1, at+1) is SARSA.
3.Deep Reinforcement Learning
This approach extends reinforcement learning by using a deep neural network and without explicitly designing the state space. The work on learning ATARI games by Google DeepMind increased attention to deep reinforcement learning or end-to-end reinforcement learning. Remarkably, the computer agent DeepMind has achieved levels of skill higher than humans at playing computer games. Even a complex game like DOTA 2 was won by a deep reinforcement learning network based upon DeepMind and OpenAI Gym environments that beat human players 3-2 in a tournament of best of five matches.
For more information, go through the following links:
If reinforcement learning was cutting edge data science, AutoML is bleeding edge data science. AutoML (Automated Machine Learning) is a remarkable project that is open source and available on GitHub at the following link that, remarkably, uses an algorithm and a data analysis approach to construct an end-to-end data science project that does data-preprocessing, algorithm selection,hyperparameter tuning, cross-validation and algorithm optimization to completely automate the ML process into the hands of a computer. Amazingly, what this means is that now computers can handle the ML expertise that was earlier in the hands of a few limited ML practitioners and AI experts.
AutoML has found its way into Google TensorFlow through AutoKeras, Microsoft CNTK, and Google Cloud Platform, Microsoft Azure, and Amazon Web Services (AWS). Currently it is a premiere paid model for even a moderately sized dataset and is free only for tiny datasets. However, one entire process might take one to two or more days to execute completely. But at least, now the computer AI industry has come full circle. We now have computers so complex that they are taking the machine learning process out of the hands of the humans and creating models that are significantly more accurate and faster than the ones created by human beings!
The basic algorithm used by AutoML is Network Architecture Search and its variants, given below:
Network Architecture Search (NAS)
PNAS (Progressive NAS)
ENAS (Efficient NAS)
The functioning of AutoML is given by the following diagram:
If you’ve stayed with me till now, congratulations; you have learnt a lot of information and cutting edge technology that you must read up on, much, much more. You could start with the links in this article, and of course, Google is your best friend as a Machine Learning Practitioner. Enjoy machine learning!
Interactive notebooks are experiencing a rise in popularity. How do we know? They’re replacing PowerPoint in presentations, shared around organizations, and they’re even taking workload away from BI suites. Today there are many notebooks to choose from Jupyter, R Markdown, Apache Zeppelin, Spark Notebook and more. There are kernels/backends to multiple languages, such as Python, Julia, Scala, SQL, and others. Notebooks are typically used by data scientists for quick exploration tasks.
In this blog, we are going to learn about Jupyter notebooks and Google colab. We will learn about writing code in the notebooks and will focus on the basic features of notebooks. Before diving directly into writing code, let us familiarise ourselves with writing the code notebook style!
The Notebook way
Traditionally, notebooks have been used to document research and make results reproducible, simply by rerunning the notebook on source data. But why would one want to choose to use a notebook instead of a favorite IDE or command line? There are many limitations in the current browser-based notebook implementations, but what they do offer is an environment for exploration, collaboration, and visualization. Notebooks are typically used by data scientists for quick exploration tasks. In that regard, they offer a number of advantages over any local scripts or tools. Notebooks also tend to be set up in a cluster environment, allowing the data scientist to take advantage of computational resources beyond what is available on her laptop, and operate on the full data set without having to download a local copy.
The Jupyter Notebook is an open source web application that you can use to create and share documents that contain live code, equations, visualizations, and text. Jupyter Notebook is maintained by the people at Project Jupyter.
Jupyter Notebooks are a spin-off project from the IPython project, which used to have an IPython Notebook project itself. The name, Jupyter, comes from the core supported programming languages that it supports: Julia, Python, and R. Jupyter ships with the IPython kernel, which allows you to write your programs in Python, but there are currently over 100 other kernels that you can also use.
Why Jupyter Notebooks
Jupyter notebooks are particularly useful as scientific lab books when you are doing computational physics and/or lots of data analysis using computational tools. This is because, with Jupyter notebooks, you can:
Record the code you write in a notebook as you manipulate your data. This is useful to remember what you’ve done, repeat it if necessary, etc.
Graphs and other figures are rendered directly in the notebook so there’s no more printing to paper, cutting and pasting as you would have with paper notebooks or copying and pasting as you would have with other electronic notebooks.
You can have dynamic data visualizations, e.g. animations, which is simply not possible with a paper lab book.
One can update the notebook (or parts thereof) with new data by re-running cells. You could also copy the cell and re-run the copy only if you want to retain a record of the previous attempt.
Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.
Why Google Colab
As the name suggests, Google Colab comes with collaboration backed in the product. In fact, it is a Jupyter notebook that leverages Google Docs collaboration features. It also runs on Google servers and you don’t need to install anything. Moreover, the notebooks are saved to your Google Drive account.
Some Extra Features
1. System Aliases
Jupyter includes shortcuts for common operations, such as ls and others.
2. Tab-Completion and Exploring Code
Colab provides tab completion to explore attributes of Python objects, as well as to quickly view documentation strings.
3. Exception Formatting
Exceptions are formatted nicely in Colab outputs
4. Rich, Interactive Outputs
Until now all of the generated outputs have been text, but they can be more interesting.
5. Integration with Drive
Colaboratory is integrated with Google Drive. It allows you to share, comment, and collaborate on the same document with multiple people:
Differences between Google Colab and Jupyter notebooks
1. Infrastructure Google Colab runs on Google Cloud Platform ( GCP ). Hence it’s robust, flexible
2. Hardware Google Colab recently added support for Tensor Processing Unit ( TPU ) apart from its existing GPU and CPU instances. So, it’s a big deal for all deep learning people.
3. Pricing Despite being so good at hardware, the services provided by Google Colab are completely free. This makes it even more awesome.
4. Integration with Google Drive Yes, this seems interesting as you can use your google drive as an interactive file system with Google Colab. This makes it easy to deal with larger files while computing your stuff.
5. Boon for Research and Startup Community Perhaps this is the only tool available in the market which provides such a good PaaS for free to users. This is overwhelmingly helpful for startups, the research community and students in deep learning space
Working with Notebooks — The Cells Based Method
Jupyter Notebook supports adding rich content to its cells. In this section, you will get an overview of just some of the things you can do with your cells using Markup and Code.
There are technically four cell types: Code, Markdown, Raw NBConvert, and Heading.
The Heading cell type is no longer supported and will display a dialogue that says as much. Instead, you are supposed to use Markdown for your Headings.
The Raw NBConvert cell type is only intended for special use cases when using the nbconvert command line tool. Basically, it allows you to control the formatting in a very specific way when converting from a Notebook to another format.
The primary cell types that you will use are the Code and Markdown cell types. You have already learned how code cells work, so let’s learn how to style your text with Markdown.
Styling Your Text
Jupyter Notebook supports Markdown, which is a markup language that is a superset of HTML. This tutorial will cover some of the basics of what you can do with Markdown.
Set a new cell to Markdown and then add the following text to the cell:
When you run the cell, the output should look like this:
If you would prefer to bold your text, use a double underscore or double asterisk.
Creating headers in Markdown is also quite simple. You just have to use the humble pound sign. The more pound signs you use, the smaller the header. Jupyter Notebook even kind of previews it for you:
Then when you run the cell, you will end up with a nicely formatted header:
You can create a list (bullet points) by using dashes, plus signs, or asterisks. Here is an example:
Code and Syntax Highlighting
If you want to insert a code example that you don’t want your end user to actually run, you can use Markdown to insert it. For inline code highlighting, just surround the code with backticks. If you want to insert a block of code, you can use triple backticks and also specify the programming language:
Useful Jupyter Notebook Extensions
Extensions are a very productive way of enhancing your productivity on Jupyter Notebooks. One of the best tools to install and use extensions I have found is ‘Nbextensions’. It takes two simple steps to install it on your machine (there are other methods as well but I found this the most convenient):
Step 1: Install it from pip:
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install--user
Once you’re done with this, you’ll see a ‘Nbextensions’ tab on the top of your Jupyter Notebook home. And voila! There are a collection of awesome extensions you can use for your projects.
There is a thing called JupyterHub which is the proper way to host a multi-user notebook server which might be useful for collaboration and could potentially be used for teaching. However, I have not investigated this in detail as there is no need for it yet. If lots of people start using jupyter notebooks, then we could look into whether JupyterHub would be of benefit. Work is also ongoing to facilitate real-time live collaboration by multiple users on the same notebook — more information is available here and here.
Jupyter notebooks are useful as a scientific research record, especially when you are digging about in your data using computational tools. In this lesson, we learned about Jupyter notebooks. To add, in Jupyter notebooks, we can either be in insert mode or escape mode. While in insert mode, we can edit the cells and undo changes within that cell with cmd + z on a mac or ctl + z on windows. In escape mode, we can add cells with b, delete a cell with x, and undo deletion of a cell with z. We can also change the type of a cell to markdown with m and to Python code with y. Furthermore, we can have our code in a cell executed, we need to press shift + enter. If we do not do this, then the variables that we assigned in Python are not going to be recognized by Python later on in our Jupyter notebook.
Jupyter notebooks/Google colab are more focused on making work reproducible and easier to understand. These notebooks find the usage in cases where you need story telling with your code!
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Python and R have been around for well over 20 years. Python was developed in 1991 by Guido van Rossum, and R in 1995 by Ross Ihaka and Robert Gentleman. Both Python and R have seen steady growth year after year in the last two decades. Will that trend continue, or are we coming to an end of an era of the Python-R dominance in the data science segment? Let’s find out!
Python in the last decade has grown from strength to strength. In 2013, Python overtook R as the most popular language used fordata science, according to the Stack Overflow developer survey (Link).
Will Python’s Dominance Continue?
We believe, yes, definitely. Two words – data science.
Data science is such a hot and happening field right now, and the data scientist job is hyped as the ‘sexiest job of the 21st century‘, according to Forbes. Python is by far the most popular language for data science. The only close competitor is R, which Python overtook in the KDNuggets data science survey of 2016 . As shown in the link, in 2018, Python held 65.6% of the data science market, and R was actually below RapidMiner, at 48.5%. From the graphs, it is easy to see that Python is eating away at R’s share in the market. But why?
In 2018, we say a huge push towards advancement across all verticals in the industry due to deep learning. And what is the most famous tool for deep learning? TensorFlow and Keras – both Python-based frameworks! While we have Keras and TensorFlow interfaces in R and RStudio now, Python was the initial choice and is still the native library – kerasR and tensorflow in RStudio being interfaces to the Python packages. Also, a real-life implementation of a deep learning project contains more than the deep learning model preparation and data analysis.
There is the data preprocessing, data cleaning, data wrangling, data preparation, outlier detection and missing data values management section which is infamous for taking up 99% of the time of a data scientist, with actual deep learning model work taking just 1% or less of their on-duty time! And what language is used for this commonly? For general purpose programming, Python is the goto language in most cases. I’m not saying that R doesn’t have data preprocessing packages. I’m saying that standard data science operations like web scraping are easier in Python than in R.And hence Python will be the language used in most cases, except in the statistics and the university or academic fields.
Our prediction for Python – growth – even to 70% of the data science market as more and more research-level projects like AutoML keep using Python as a first language of choice.
What About R?
In 2016, the use of R for data science in the industry was 55%, and Python stood at 51%. Python increased by 33% and R decreased by 25% in 2 years. Will that trend continue and will R continue on its downward spiral? I believe perhaps in figures, but not in practice. Here’s why.
Data science is at its heart, the field of the statistician. Unless you have a strong background in statistics, you will be unable to process the results of your experiments, especially in concepts like p-values, tests of significance, confidence intervals, and analysis of experiments. And R is the statistician’s language.Statistics and mathematics students will always find working in R remarkably easy and simple, which explains its popularity in academia. R programming lends itself to statistics. Python lends itself to model building and decent execution performance (R can be 4x slower). R, however, excels in statistical analysis. So what is the point that I am trying to express?
Simple – Python and R are complementary. They are best used together. You will find that knowledge of both Python and R will suit you best for most projects. You need to learn both. You can find this trend expressed in every article that speaks about becoming a data science unicorn – knowledge of both Python and R is required as a norm.
Yes, R is having a downturn in popularity. However, due to the complementary nature of the tools, I believe that R will have a part to play in the data scientist’s toolbox, even if it does dip a bit in growth in the years to come. Very simply, R is too convenient for a statistician to be neglected by the industry completely. It will continue to have its place in the toolbox. And yes; deep learning is now practical in R with support for Keras and AutoML as well as of right now.
Dimensionless Technologies is the market leader as far as training in AI, cloud, deep learning and data science in Python and R is concerned. Of course, you don’t have to spend 40k for a data science certification, you could always go for its industry equivalent – 100-120 lakhs for a US university’s Ph.D. research doctorate! What Dimensionless Technologies has as an advantage over its closest rival – (Coursera’s John Hopkins University’s Data Science Specialization) – is:
Live Video Training
The videos that you get on Coursera, edX, Dataquest, MIT OCW (MIT OpenCourseWare), Udacity, Udemy, and many other MOOCs have a fundamental flaw – they are NOT live! If you have a doubt in a video lecture, you only have the comments as a communication tool to the lectures. And when over 1,000 students are taking your class, it is next to impossible to respond to every comment. You will not and cannot get personalized attention for your doubts and clarifications. This makes it difficult for many, especially Indian students who may not be used to foreign accents to have a smooth learning curve in the popular MOOCs available today.
Try Before You Buy Fully
Dimensionless Technologies offers 20 hours of the course for Rs 5000, with the remaining 35k (10k of 45k waived if you qualify for the scholarship) payable after 2 weeks / 20 hours of taking the course on a trial basis. You get to evaluate the course for 20 hours before deciding whether you want to go through the entire syllabus with the highly experienced instructors who are strictly IIT alumni.
Instructors with 10 years Plus Industry Experience
In Coursera or edX, it is more common for Ph.D. professors than industry experienced professionals to teach the course. If you are good with American accents and next to zero instructor support, you will be able to learn a little bit about the scholastic side of your field. However, if you want to prepare for a 100K USD per year US data scientist job, you would be better off learning from professionals with industry experience. I am Not criticizing the Coursera instructors here, most have industry experience as well in the USA. However, if you want connections and contacts in the data science industry in India and the US, you might be a bit lost in the vast numbers of student who take those courses. Industry experience in instructors is rare in a MOOC and critically important to your landing a job.
Personalized Attention and Job Placement Guarantee
Dimensionless has a batch size of strictly not more than 25 per batch. This means that unlike other MOOCs with hundreds or thousands of students, every student in a class will get individual attention and training. This is the essence of what makes this company the market leader in this space. No other course provider has this restriction, which makes it certain that when you pay the money, you are 100% certain of completing your course, unlike all the other MOOCs out there. You are also given training for creating a data science portfolio, and how to prepare for data science interviews when you start applying to companies. The best part of this entire process is the 100% job placement guarantee.
If this has got your attention, and you are highly interested in data science, I encourage you to go to the following link to see more about the Data Science Using Python and R course, a strong foundation for a data science career: