Machine learning is a form of Artificial Intelligence (AI) that allows computer systems able to study without being explicitly programmed. It is concerned with developing softwares that improves from past experience, used in programs for classification and prediction. Predictions are made on the basis of visual data and measurement data. This extraction of data is done by machines through statistical analysis.
Machine learning focuses on the development of computer applications that may exchange while exposed to new information. It can be split into three categories: Supervised learning (algorithms are trained using labeled examples where desired output is known), Unsupervised learning (used against data that has no historical labels) and Reinforcement learning (uses trial and error actions which yield the greatest rewards).
After development of algorithms in Machine Learning there are some advantages of machine learning to notice:
Machine learning is a way of tuning a system with tunable parameters, some applications are:
Main Steps for learning toolbox are:
Getting Started: Learn the basics of Statistics and Machine Learning Toolbox.
Descriptive Statistics and Visualization: Import and export of Data, Visualization and description.
Probability Distribution: Parameter generation, Data Frequency models and Random Sample Generation.
Hypothesis Tests: It includes t-test, F-test, chi-square goodness-of-fit test, and more.
Supervised Learning: It includes classification, Linear Regression, KNN, NN classifier.
Unsupervised Learning: It includes clustering analysis, Anova and Regression algorithms.
Dimensionality Reduction: It includes nonnegative matrix factorization, factor analysis, sequential feature selection and PCA.
Statistics of Industrial: survival and reliability analysis, Design of experiments (DOE) statistical process control.
Machine learning can be applied to complex research fields such as quality improvement and its approaches are of particular interest considering steadily increasing search outputs and accessibility of the existing evidence is a particular challenge of the research field in quality improvement. Machine learning allow improved predictive performance.
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Python Overview: Introduction features.
Basic Syntax:Interactive mode programming, script mode programming, identifiers, line and indentation, quotation, comment and command line arguments in python.
Variables Type: Assigning value to a variable, multiple assignment, standard dataypes, number, string,list, tuple, dictionary, data type conversion.
Basic Operators: Arithmetic operators, comparison operators, assignment operators, bitwise operators,logical operators, membership operators, identity operators.
Decision Making: Single statement suites.
Python Loops: Loops (while, for, nested), control statement of loops.
Number (Number: int, long, float, complex): Assigning value to a number, delete the reference to a number, number type conversion, mathematical functions, random number functions, trigonometric functions, mathematical constants.
String: Accessing values in string, updating strings, escape characters, string special characters, string special operators, string formatting operator, triple code, unicode string, built in string methods.
List: Basic list operations, indexes, accessing values in list, updating list, delete list elements.
Tuple: Basic tuple operations, indexing, accessing values in tuple, updating tuple, delete tuple element.
Dictionary: Accessing values in dictionary, updating dictionary, delete dictionary elements, list under dictionary, dictionary under list, sorting in dictionary.
Date and Time: Tick, time tuple, current time, getting formatted time, getting calender.
Python Functions: Defining a function, calling a function, overloading concept, function arguments, required arguments, keyword arguments, default arguments, variable length arguments, anonymous function, return statements, concept of variables.
Concept of OOPs: Classes and objects, overview of OOP terminology, creating classes, creating instance objects, accessing attributes, built in class attributes, destroying objects, class inheritance, overriding methods, overloading operators, data hiding, Encapsulation, data abstraction, polymorphism.
Module: Import statements, from import, from import * statement, locating modules, PYTHONPATH variable, namespace and scoping, dir() function, reload() function, packages in python.
Exception: Exception handling, assert statement, except clause, try finally clause, argument of exception, raising exception, user defined exception.
Machine Learning basics: Machine Learning, Key terminology, key task of machine learning.
Choosing right algorithm, steps in developing a machine learning algorithm, perceptron model, MCPModel
Classifying with k-Nearest Neighbors (Duration 2 hours): Classifying with distance measurement, examples.
Tree (Duration 2 hours): Tree construction, plotting trees, testing and storing classifier, examples.
Classification with probability theory (Duration 2 hours): Bayesian decision theory, conditional probability, classification with Bayesian and conditional probability, examples.
Logistic regression (Duration 2 hours): Classification with logistic regression and sigmoid function, optimization techniques to find best regression coefficients, examples.
Predicting value with regression (Duration 2 hours): Best fit line, locally weighted linear regression, ridge regression, The bias/variance trade off, examples.
Support vector machine (Duration 4 hours): Separating data, maximum margin, hyperplane, kernel approach (parameters), testing and training data, AdaBoost, examples.
Artificial Neural Network (Duration 4 hours): Deep learning (Theano, tensor flow and keras).
Grouping unlabeled data with k-means clustering (Duration 2 hours): k-means clustering algorithm, improving cluster performance with post processing, bisection k-means, examples.
Hierarchical clustering (Duration 2 hours): Cluster dissimilarity, agglomerative and Divisive strategies.
Neural Networks (Duration 2 hours): Hebbian learning, generative adversarial networks and its application, examples.
Latent variable models (Duration 2 hours): Expectation-maximization algorithm, methods of moments, blind signal separation.
Flask Overview: Web Framework, Flask, prerequisite, installation, applications.
Flask Routing, Variable Rules and URL Building: route(), local host, variable name , converters and description, url_for(), steps involved in URL building, examples
Http Methods: Http Protocol, GET, HEAD, POST, PUT, DELETE, examples
Templates: Template engine, render_template(), web template system, jinga2, examples
Request Objects: Flask module, form, args, Cookies, files, method, examples
Cookies and Sessions: get() method, userID, examples.
Message Flashing: next, flash(), template calling, examples.
File Uploading: handling flask upload, define path, examples.
Mail: Flask-Mail, parameters and description, mail class and methods, examples.
SQLite: SQLite, database, view function, flask-SQLite application, examples.
MySql: Database, view function, examples.
Deployment: External visible server, heroku, examples.