What are pre-requisites to start learning Machine learning?

Machine Learning, is a buzz word in India and around the world. The concept is definitely a business class flight ticket to the data analysis and regression studies. The proliferation in data science, computer power and tendency to make machine smart is what makes machine learning the future.But like humans, machine learning is simply learning from the past.

So, what are pre-requisites of “learning” machine learning?

The programming language:

(For API)

You must have experience in any of the programming languages so that you are used to on how and in what way the algorithms and syntaxes work.

Experts and I would suggest that you ought to have programming skills in python. There are various platforms that are based on python and the libraries which are used by various sub fields machine learning consists. So when you are starting to gain experience in machine learning or Artificial intelligence as a whole, you must give your initial time to learn python. Frequently used platforms are Pycharm, Pandas and Tensorflow etc. A glance about data structures and algorithms would be a plus point to a protege starting to learn machine learning.

Some other proprietary languages like octave and MATLAB are sometimes used for kick-starting your study in machine learning.

The Mathematics:

(For Algorithms)

To make computer understand things, you must make it work on choosing, predicting and analysing data. The various sub sections of mathematics that are used in machine learning are:

  • Statistics: As referenced, machine learning is all about making your computer/ processor learn. The basics of which starts with sending a lot and lot of data to it. The importance of statistics comes in as statistics is the core of providing data about data. Machine learning is hence also termed as “Applied statistics”. For starters, begin with the knowledge of Bayesian statistics.
  • Probability: It is known for finding the chances for results, inputs and premises that the computer must consider. Conditional probabilities, Bayes theorem and Correlation theories are often used in machine learning algorithms.
  • Linear Algebra: It is the mathematics of 21st Vectors, matrices and linear equations are the components of linear algebra which are used as crux in optimisation techniques in machine learning.
  • Optimization techniques: The algorithms that are used to make your computer actually “Learn” must be stable and efficient enough, taking minimum time to provide accurate and precise output.
  • Calculus: The major part of providing optimal solutions from the perspective of machine is through differential and multivariate calculus. Some algorithms may require to find probabilities using integral calculus like the finding the posterior of Bayesian model.
  • There are other sub sections that will be needed in machine learning algorithms. The best part of it is, internet is a hub of everything and you can learn these concepts and techniques very easily.

The Enthusiasm: For starters, just focus on implementations of machine learning. The eagerness to learn, implement and solve tough but common problems is the most important aspect of concept behind machine learning as a whole. Even if you don’t know anything from the above, you can learn it afresh. Heads up, start exploring.

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Should a machine learning beginner go straight for deep learning?

Artificial Intelligence is a buzzword which is on a voyage to replace most of the human work efforts in the subsequent upcoming years. It is a giant circle of a pool of various applications to make a machine intelligent.

Machine learning is a one of the subsets of AI. Representation learning is a subset of Machine learning. And Deep learning is the crux of Representation learning. Hence, going into deep learning before getting an overview of Machine learning can be non-fruitful. Although, it depends on your end goal for which you started the subject. Why do you even want to learn machine learning and its subclasses?

To answer your question, yes! You can start directly with deep learning as well, but it will need some basics of machine learning. Deep represents a dense network with number of layers. It is teaching machine to actually learn like humans, self-adapt and sustain. Deep learning does not require manual input of anything. It needs a large number of data or “big data.” Big data is a separate theory that you will need to understand about before you kick start with deep learning.

Deep learning is inspired by human nature and has feature detection applications. The base of deep learning is neural network present at different layers. Deep learning is a buzz because it’s uniqueness of using human brain and neural coding as a basis to make computer identify, classify and make use of the stimulus/stimuli given to it.

Professionals recommend the learning path of machine learning to be followed first.  Basics of mathematics like algebra and probability, a bit of statistics, python and probably a basic course implementation of machine learning is suggested.

There is this book named Deep learning Book by Goodfellow, Bengio and Courville, which takes you to deep learning via the basics. This can be a presumed shortcut to directly jump to deep learning without learning much about machine learning. Bayesian Deep learning is another aspect crucial to deep learning protégé.

Although, as the protege is shifting towards the practical approach to study, the competition among Machine learning crash courses and machine learning classes will be at its peak. Practical knowledge par with your limits of imagination and capacity to think. The whole AI and ML and deep learning are things more of a buzz rather than people actually working on it. If you are really interested in the mechanisms, start reading theories and implementing algorithms now.


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How should one start learning about AI and machine learning?

AI is definitely the future. Machine learning, being the current application of artificial intelligence, is based on the idea to give the computer access to data and make them learn themselves. There are obviously various ways to start.

These are the steps you are suggested to follow:

  • KNOW THE STRUCTURE: Machine learning and AI as a whole, have a crux known as data science. So, before getting into deeper nodes of this web, you must start with the basics. To apply machine learning, you got to answer these questions; what to achieve and why to achieve it. Collection, storage and analysis of data using SQL and python will be helpful. This can be done using online study materials.


  • ANALYSE THE DATA: Next, jump to the most important characteristic of machine learning course, mathematics. Clear the basic concepts of statistics, probability and calculus as well. You can start with Khan Academy course on python statistics, covering various topics. You have to pull up resources, and try to make best use out of them.


  • ALGORITHMS: Once you are familiar with what and why, you must start input your time in theory and algorithms that are used while you are creating a specific application of machine learning. Start with regressions, decision trees, Bayesian models and you will be choosing algorithms based on them.


  • IMPLEMENTATION: Once you are clear with basics and algorithms, start implementation of it. Start learning the python libraries that you must use and will be using to apply the theories you learned. You will get everything piled up in scikit learn, best API with simple and efficient set of tools for data mining and analysis. There are a bunch of libraries to start with like numpy, Matplotlib, Pandas etc.


  • TARGET: Machine learning is more about practical application rather than theory. So, once you are clear with the fundamentals, select an application and start exploring about it. You might need to read and learn something extra down the line, but your knowledge will be practical based. Take up projects, set deadlines and start exercising whatever you learnt.


There are two broad perspectives of getting into AI and machine learning; first, the API and second, the algorithms. These two prospects are hardly covered when you start an online course or you read a book. The best way to gain knowledge is by execute it in practical ways. Various Machine learning classes in Pune have been opened since.

The School of Digital marketing has shown interests in AI and Machine learning and is ready to make you master in the applications, projects and implementations of machine learning in the market. You can visit our website and know more about other details in case you show interest in joining the course.

Some recommendations to start from:


Bayesian Reasoning and Machine learning by David Barber

Machine learning: A probabilistic perspective by Kevin Murphy

Data science from scratch: First principles with Python by Joel Grus’

MOOCs (Massive open online courses)

Andrew Ng Machine learning at coursera

Sebastian and Katie at Udacity

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5 Free Must-Read Books for Machine Learning and Data Science

 1. Python Data Science Handbook

By Jake VanderPlas

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A Whirlwind Tour of Python: it’s a fast-paced introduction to the Python language aimed at researchers and scientists.

  1. Neural Networks and Deep Learning
    By Michael Nielsen

Neural Networks and Deep Learning is a free online book. The book will teach you about:

  • Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
  • Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

  1. Think Bayes
    By Allen B. Downey

Think Bayes is an introduction to Bayesian statistics using computational methods.

The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.

Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops.

  1. Machine Learning & Big Data
    By Kareem Alkaseer

This is a work in progress, which I add to as time allows. The purpose behind it is to have a balance between theory and implementation for the software engineer to implement machine learning models comfortably without relying too much on libraries. Most of the time the concept behind a model or a technique is simple or intutive but it gets lost in details or jargon. Also, most of the time existing libraries would solve the problem at hand but they are treated as black boxes and more often than not they have their own abstractions and architectures that hide the underlying concepts. This book’s attempt is to make the underlying concepts clear.

  1. Statistical Learning with Sparsity: The Lasso and Generalizations
    By Trevor Hastie, Robert Tibshirani, Martin Wainwright

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. This book describes the important ideas in these areas in a common conceptual framework.

Machine Learning Is the Future of Marketing. It is one of the booming technologies in today’s date. If you are looking for machine learning courses in pune then you can visit Its-120 hour’s certification course providing in-depth exposure to Data Science, Big Data, Machine and Deep Learning.

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Top 5 Recent Research on Machine Learning and Deep Learning

Machine learning and Deep Learning research advances are transforming our technology. Here are the 5 most important (most-cited) scientific papers that have been published since 2014, starting with “Dropout: a simple way to prevent neural networks from overfitting”.

Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billions of people. The research in this field is developing very quickly and to help our readers monitor the progress we present the list of most important recent scientific papers published since 2014.

1. Dropout: a simple way to prevent neural networks from overfitting, by Hinton, G.E., Krizhevsky, A., Srivastava, N., Sutskever, I., & Salakhutdinov, R. (2014). Journal of Machine Learning Research, 15, 1929-1958. (cited 2084 times, HIC: 142 , CV: 536)

2. Deep Residual Learning for Image Recognition, by He, K., Ren, S., Sun, J., & Zhang, X. (2016). CoRR, abs/1512.03385. (cited 1436 times, HIC: 137 , CV: 582).
Summary: We present a residual learning framework to ease the training of deep neural networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, by Sergey Ioffe, Christian Szegedy (2015) ICML. (cited 946 times, HIC: 56 , CV: 0).
Summary: Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change.  We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs.  Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.

4. Large-Scale Video Classification with Convolutional Neural Networks , by Fei-Fei, L., Karpathy, A., Leung, T., Shetty, S., Sukthankar, R., & Toderici, G. (2014). IEEE Conference on Computer Vision and Pattern Recognition (cited 865 times, HIC: 24 , CV: 239)
Summary: Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes .

5. Microsoft COCO: Common Objects in Context , by Belongie, S.J., Dollár, P., Hays, J., Lin, T., Maire, M., Perona, P., Ramanan, D., & Zitnick, C.L. (2014). ECCV. (cited 830 times, HIC: 78 , CV: 279) Summary: We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

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