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Things who should know before start learning Python

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Application of Machine Learningf

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these are the things you should known prior to start learning machine learning

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Machine Learning process and scenarios

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Python development trends

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http://pythonandmltrainingcourses.com/courses/best-machine-learning-course-in-delhi/

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Machine Learning Methods

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.

Which machine learning algorithms you should use? A lot depends on the characteristics and the amount of available data, as well as your training  goals, in each particular use case. Avoid using complicated algorithms unless the ends justify more expensive means and resources. Here are some of the common algorithms ranked by ease of use. And if you want to learn more about Machine learning  go through this link best machine learning course in Delhi.

1. Decision trees

Decision tree analysis typically uses a hierarchy of variable decision nodes that, when answered, step by step , can classify a given customer is creditworthy or not.Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves. The leaves are the decisions or the final outcomes. And the decision nodes are where the data is split.

2. Support vector Machines 

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimensional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.“Support Vector Machine “ (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However,  it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is the number of features you have) with the value of each feature being the value of a particular coordinate. 

3.Regression 

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship between dependent and independent variables, they are considering and the number of independent variables being used.Regression maps the behavior of the dependent variable relative to one or more dependent variables. Regression is useful in identifying continuous relationship between variables.

4. Naive Bayes Classification 

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.Using Bayes theorem, we can find the probability of A happening, given that B has occurred. Here, B is the evidence and A is the hypothesis. The assumption made here is that the predictors/features are independent. That is presence of one particular feature does not affect the other. Hence it is called naive.

5. Random forest 

Random Forest is the go to machine learning algorithm that uses a bagging approach to create a bunch of decision trees with random subset of the data. A model is trained several times on random sample of the dataset to achieve good prediction performance from the random forest algorithm.In this ensemble learning method, the output of all the decision trees in the random forest, is combined to make the final prediction. The final prediction of the random forest algorithm is derived by polling the results of each decision tree or just by going with a prediction that appears the most times in the decision trees.

6.Recurrent neural network 

A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

7. Convolutional neural network 

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery.CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "fully-connectedness" of these networks makes them prone to overfitting data. Typical ways of regularization include adding some form of magnitude measurement of weights to the loss function. However, CNNs take a different approach towards regularization: they take advantage of the hierarchical pattern in data and assemble more complex patterns using smaller and simpler patterns. Therefore, on the scale of connectedness and complexity, CNNs are on the lower extreme.

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Python training in Noida

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Everything You Ever Wanted to Know About Machine Learning With Python

We are living in the ‘age of data’ that is enriched with better computational power and more storage resources. This data or information is increasing day by day, but the real challenge is to make sense of all the data. Businesses & organizations are trying to deal with it by building intelligent systems using the concepts and methodologies from Data science, Data Mining and Machine learning. Among them, machine learning is the most exciting field of computer science. It would not be wrong if we call machine learning the application and science of algorithms that provides sense to the data.

Before going inside of Article let's first understand what python is and what Machine Learning is ?

Python

Python is a widely used high-level programming language for general-purpose programming

Python is an interpreted, object-oriented programming language that has gained popularity because of its clear syntax and readability. Python was created by Guido van Rossum.  This means that it is a programming language which can be run on any system (ex. Linux). The main component of this language is called an object.

Machine Learning

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.  Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. We use algorithms and models to achieve this.

 Clearly, Machine Learning with Python it is a part of Artificial Intelligence. Using it, we can enable a machine to learn how to do a task on its own without explicitly telling it what to do and when. In other words we program it to think for itself instead of just programming it to act.

Python is one of the programming languages used to create Artificial Intelligence. Python community has developed many modules to help programmers implement machine learning. There are various ML algorithms, techniques and methods that can be used to build models for solving real-life problems by using data.

Apart from enjoying huge popularity in different areas of software development, Python has obtained a leading position in the machine learning domain today. The combination of simplicity, shorter development time, and consistent syntax make Python well-suited for projects in the field of machine learning. Anaconda is the version of Python that is supported by all commonly used OSs like Windows, Linux etc. It offers a complete package for machine learning that includes scikit-learn, matplotlib and NumPy. If you don’t have any prior knowledge of programming, there’re lots of online resources, books etc that can help you obtain the fundamental knowledge.

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