A Comprehensive Guide To Machine learning

January 21, 2020

Artificial intelligence is shaping our future with more might and power than any other technology or thing, for that matter. Sensors and smart things are having a profound impact on how we perform basic functions using tech-enabled devices. Its critical discipline, machine learning, is also growing in popularity- unleashing a wide array of applications across industries.

Machine learning is the art and science of teaching computers to learn from provided data and make decisions or predictions. In its true essence, with machine learning, a computer device should be able to devise patterns without being explicitly taught to.

Machine learning is one use case of the infrastructure that can handle big data. A few places where we can see machine learning in action are:

  • Supervised learning - Your email provider places every fishy email coming from bogus sources into the spam folder. A supervised learning algorithm takes labeled data and creates a model on its own to make predictions on new data. 
  • Unsupervised learning - Businesses use hundreds of demographics and behavioral factors to segment customers into distinct groups. Unsupervised learning happens when the data is not labeled or categorized. It is upon the machine to spot patterns in the data, classify it, and derive meaning. 
  • Reinforcement learning - Sensors, computer, and camera within a self-driving car integrate to form an environment where they can navigate a city without a human. Reinforcement technique uses a reward system of trial and error in order to optimize the long-term reward.

If we’ve got you excited, let’s dig deep into the world of machine learning and understand how the technology works, what makes it so special, and what the future holds for you.

The Current State of Machine Learning

AI augmentation is expected to hit $2.9 trillion in revenue generation by 2021, freeing the global workforce of 6.2 billion hours of productivity, Gartner predicts. Machine learning sits at the center of digital transformation as it backs up useful technologies such as robotics, process automation, and speech recognition. Therefore, within the AI category, ML will attract a majority of investment.

Innovations in industries courtesy Machine Learning-

  • Banking - ML analytics can empower banks and other financial institutions to leverage advanced analytics and learn more about their customers, more efficiently. By analyzing customer behavior and tailoring services and products to meet their needs, companies previously saw an average increase of $369,000,000 per year in their revenue, according to McKinsey.
  • Energy - Machine learning, in conjunction with deep learning will prove efficient to combat climate change by encouraging efficient energy utilization. Businesses are already using ML to sell solar panels and researchers predict that cloud-based monitoring systems will optimize energy in real-time soon!
  • Retail - Machine learning can make a retail store’s experience dynamic and personalized. Convenient and customized brand encounters will improve profits and sales for retail stores. 
  • Healthcare - AI and ML are strong tools to help doctors and healthcare institutions. In one case study, a group of researchers used ML to predict whether patients would be hospitalized due to heart disease. They were able to achieve 82% accuracy rate which was 26% better and more accurate than the existing prediction models.

Get Started with Machine Learning

Machine learning, by its name, can appear too intimidating without a gentle introduction to its prerequisites. We suggest you don’t overdo this part, though. Unless you want to be a Ph.D. at machine learning, a bit of knowledge can do you good. The roadmap to learn machine learning goes along the following lines:

  • The right place to begin is to revise linear algebra. Pay particular attention to the core concepts including vectors, matrix multiplication, Eigenvectors, and determinants- these are what make machine learning algos work.
  • After that, calculus is your next battleground. Learn and understand the meaning of derivatives, and how they are used for optimization. Learn everything you can on single variable calculus and at least the basics of multivariable calculus.
  • Learn Python for data science. Go through all libraries used in machine learning coding, such as Pandas, Numpy, SKLearn, Matplotlib. Machine learning leverages these tools.
  • Understand statistics for data science, mainly Bayesian probability. 
  • Go through ML in theory. You might want to argue if it is worth the effort and time to go through the theory when you can make things work only by learning about ML packages. But, anyone who wants to apply ML in their work should have a firm grasp on the fundamentals. Learn machine learning theory to:
  • Plan and collect data - Data collection can be a time-consuming process. Every ML engineer should have a firm grip on what data they need and how much.
  • Make the right data assumptions and preprocess data - Different algorithms make different assumptions about any set of data. You should know how to preprocess your data, whether or not to normalize it, and how to make your data robust to missing information.
  • Interpret model results - ML is not a black-box. Surely, not all results can be interpreted, but you need to analyze your model in order to improve it. Therefore, gather in-depth understanding to rate your model as overfit and underfit, gauge the room for improvement, and explain business results to stakeholders.
  • Improve and tune-in your models - You need to know about the various regularization methods and tuning parameters to answer basic questions about whether your model needs feature-engineering or data collection, or if you can ensemble your models.
  • Drive business value - ML projects cannot be done in a vacuum. You need to understand the tools you need, and what goals you are trying to achieve through them. Which metrics are important to your business? 
  • Practice toward targeted projects. There are three levels to achieve targeted practice with machine learning algorithms and concepts:
  • Practice the machine learning workflow including data collection, cleaning, preprocessing, model building, fine-tuning, and testing and evaluation.
  • Practice on real datasets to gain a deeper understanding of which models are appropriate for which kinds of challenges.
  • Dive deep into individual topics and learn about clustering algorithms, and so on by utilizing and exercising your knowledge.
  • Now you are ready to take the ML world by storm with some really cool and happening projects. Consider the Titanic Survivor Prediction problem or any such challenging statement that truly tests what you’ve learned.

Here are a few tools and technologies you need to equip yourself with to gain an edge over the competition and solidify your expertise with ML:

  • Python libraries - NumPy, Pandas, SciKit-Learn, SciPy, Seaborn, Matplotlib
  • Algorithms - Linear regression, Logistic regression, Naive Bayes Classifier, K - Nearest Neighbors, K - Means, Support Vector Machine, Decision Trees, Random Forests, Gradient Boosting

Future Predictions with ML at the Core

Without a doubt, ML will leave a strong impact on how operations and processes are managed in the enterprises of tomorrow. Already, ML is changing the dynamics in enterprises and causing companies to mix and match people and software to get things done.

The idea that AI is a simple robot program to do repetitive tasks is no more. Machine learning and AI are opening new channels of applications in various industries. It is safe to predict that the human-machine interaction will boost as a result of the demand for more and more sophisticated ML solutions.

Further, thanks to ML, we will experience a rise in AI assistants and how frequently we communicate with one. This year and the next will be imperative for us to see what these assistants are capable of. Companies such as Hyundai and Kia are planning to integrate AI assistants in their vehicles, starting this year.

Many more surprising and interesting use cases of ML await us in the future.

We are only seeing the tip of the iceberg when it comes to the potential AI and ML have to revolutionize the way we operate and perform. This is just the beginning of the wave of advancements we are expecting. Technology is only going to get faster and sharper!

Click here to know more about E2E GPU Cloud for machine learning & deep earning.

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The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

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You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

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All these metrics tell you how well you will be able to grow your business and revenue.

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You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

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If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

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By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

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Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

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Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
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In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

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Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

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