MACHINE LEARNING WITH PYTHON<\/h2>
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Machine learning is about extracting knowledge from data. It is a research field at\u00a0the intersection of statistics, artificial intelligence, and computer science and is also\u00a0known as predictive analytics or statistical learning. Python has become the lingua\u00a0franca for many data science applications. It combines the power of general purpose\u00a0programming languages with the ease of use of domain-specific scripting\u00a0languages.<\/p>\n
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Python has libraries for data loading, visualization, statistics, natural language processing, image processing, and more. This vast toolbox provides data scientists with a large array of general- and special-purpose functionality. Machine learning and data analysis are fundamentally iterative processes, in which the data drives the analysis. It is essential for these processes to have tools that allow quick iteration and easy interaction. As a general-purpose programming language, Python allows for exactly that including the creation of complex graphical user interfaces (GUIs) and web services, and for integration into existing systems.<\/p>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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What we will learn ?<\/span><\/span><\/h2>
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\u2022 Fundamental concepts of machine learning
\u2022 Introduction to popular ML Algorithms
\u2022 Data used for training a machine & how we represent data that is processed by\u00a0machine learning<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Content<\/span><\/span> of our program<\/h2>
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Basic Python operations & functions \u00a0<\/span>Revisiting Python Datatypes<\/span><\/li>\n
\u00a0Setting up machine learning python<\/li>\n
Installing essential machine learning libraries<\/li>\n
Concept of Numerical Computations using Python<\/li>\n
Introducing numpy<\/li>\n
Pandas library<\/li>\n
Scipy Library<\/li>\n
\u00a0Classification & Regression<\/li>\n
Sentiment Analysis & Prediction using ML<\/li>\n<\/ul>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Machine Learning using Python<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Prerequisite:<\/strong>
\u00a0 \u00a0 \u00a0 \u00a0\u2022 Logical thinking concepts
\u00a0 \u00a0 \u00a0 \u00a0\u2022 Basic concepts of Mathematics and Statistics<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 1<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tINTRODUCTION TO PYTHON\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 Introduction to Python language
\u2022 Difference between compiled and interpreted language
\u2022 Advantages of python over other languages
\u2022 Environmental setup for Python, Anaconda, Jupyter, Spider etc
\u2022 Data types of Python, numbers, string
\u2022 Run a python program in jupyter\/spider\/ipython
\u2022 Conditional statements If, elif, in python<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 2<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tLoops, functions and modules in Python\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 Loops in python, for and while loop
\u2022 Nested for loop
\u2022 Modules and functions in python
\u2022 Positional and keyword argument
\u2022 Lambda function
\u2022 Build-in and User defined modules
\u2022 Importing modules<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 3<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tDifferent Data Structures in Python\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 How to create strings in python
\u2022 Different build \u2013in functions in strings
\u2022 Indexing and slicing in string
\u2022 Lists and its functions
\u2022 Index and slice a list
\u2022 List comprehension
\u2022 Dictionary, Tuple, Set and their function
\u2022 Difference between all the data structures<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 4<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tObject oriented python\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 What is an Object oriented programming language
\u2022 Advantages of OOP over Procedural language
\u2022 Features of OOP
\u2022 Create class, object in python
\u2022 Concept of inheritance, method overloading
\u2022 Method overriding in python
\u2022 Exceptions in python<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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DATA SCIENCE AND MACHINE LEARNING<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 1<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tIntroduction to AI and Data Science\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 Artificial intelligence and its effect in our life
\u2022 Different fields of AI
\u2022 Advantages and disadvantages of AI
\u2022 Use case for AI
\u2022 What is Data Science
\u2022 Importance of Data in our life<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 2<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tIntroduction to Machine Learning\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 What is Machine learning
\u2022 The three different types of machine learning
\u2022 Supervised, unsupervised, reinforcement learning
\u2022 An introduction to the basic terminology and notations
\u2022 A roadmap for building machine learning systems
\u2022 Uses of Machine learning in real life example<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 3<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tIntroduction Numeric and Data Analysis Modules of Python\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 Introduction to Numpy
\u2022 Creating 1D and 2D Numpy array
\u2022 Slicing and indexing of Numpy Array
\u2022 Operations on Numpy array
\u2022 Introduction to Pandas
\u2022 Series and DataFrame
\u2022 Uses of Series and DataFrame
\u2022 Operations on series and dataframe
\u2022 Read external dataset using Pandas
\u2022 Analysis on the dataset using Panads<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 4<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tVisualization library of Python\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 Introduction to Matplotlib and Seaborn
\u2022 Difference between the two
\u2022 Scatter plot using matplotlib
\u2022 Draw histogram, barchart, pie chart to any data
\u2022 Create pairplot using seaborn
\u2022 Concept of Box plot in seaborn
\u2022 Drawing of 3D graphs<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 5<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tHandling Data \u2013 Data Wrangling\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 What is Data Wrangling
\u2022 Prepare data for use
\u2022 Introduction to sklearn module of python
\u2022 Find for missing value and impute
\u2022 Fillna() and Imputer() of sklearn
\u2022 Concepts of categorical variable and its problem
\u2022 Solution for categorical problem using dummy variable, LableEncoding
\u2022 Feature Scaling and its solution using StandardScalar<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 6<\/span><\/span><\/h2>
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\n\t\t\t\t\t\t Supervised Machine learning - Regression\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 Regression and its uses
\u2022 Single and Multiple Linear Regression
\u2022 Difference between Simple and Multiple Linear Regression
\u2022 Simple linear regression with example
\u2022 Multiple linear regression with Boston dataset
\u2022 Regression using Decision Tree and Random Forest Algorithm
\u2022 Compare between all the algorithm of regression<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 7<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tSupervised Machine learning - Classification\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 Classification and its uses
\u2022 Different types of classifiers
\u2022 K Nearest Neighbour classifier (KNN)
\u2022 Apply KNN on IRIS dataset
\u2022 Logistic regression to classify some data points
\u2022 Apply Logistic regression on Titanic dataset
\u2022 Digit recognition using Logistic regression<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 8<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tPattern Recognition and PCA\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 How to recognize pattern in machine learning
\u2022 Use of MNIST dataset to recognize some hand written digits
\u2022 Feature extraction and Feature elimination
\u2022 PCA to use Dimensionality reduction
\u2022 Problem of time complexity and visualization
\u2022 Solution using PCA
\u2022 Use PCA to recognize MNIST dataset<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 9<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tAdvance of classification\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 Classify dataset using decision tree classifier
\u2022 Difference between decision tree and Random forest
\u2022 Advantage of random forest over decision tree
\u2022 Text based classification using Na\u00efve Bayes algorithm
\u2022 Classify email using Na\u00efve Bayes classifier
\u2022 CountVectorizer and TFID algorithm to solve text classification problem<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 10<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tSVM and Kernel trick\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 What is support vector machine
\u2022 Linear SVM
\u2022 Solving non linear problem using SVM
\u2022 Kernel and kernel trick to solve non linear problem
\u2022 Polynomial and RBF kernel<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 11<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tUnsupervised learning\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 What is unsupervised learning
\u2022 Types of unsupervised learning
\u2022 Concepts of clustering
\u2022 K means clustering to cluster group of datas
\u2022 Difference between K means and Hierarchical clustering<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Module 12<\/span><\/span><\/h2>
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\n\t\t\t\t\t\tIntroduction to Data Science\n\t\t\t\t\t<\/h3>\t\t\t\t
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\u2022 Data Science overview
\u2022 Data Analytics overview
\u2022 Statistical Analysis and Business Application
\u2022 Introduction to Natural language processing using Scikit learn
\u2022 Uses of Tensor Flow and Open CV in data science problem<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t<\/body>","protected":false},"excerpt":{"rendered":"
MACHINE LEARNING WITH PYTHON Machine learning is about extracting knowledge from data. It is a research field at\u00a0the intersection of statistics, artificial intelligence, and computer science and is also\u00a0known as predictive analytics or statistical learning. Python has become the lingua\u00a0franca for many data science applications. It combines the power of general purpose\u00a0programming languages with the […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"two_page_speed":[],"footnotes":""},"class_list":["post-578","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.digifoxlabs.com\/site\/wp-json\/wp\/v2\/pages\/578","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.digifoxlabs.com\/site\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.digifoxlabs.com\/site\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.digifoxlabs.com\/site\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.digifoxlabs.com\/site\/wp-json\/wp\/v2\/comments?post=578"}],"version-history":[{"count":46,"href":"https:\/\/www.digifoxlabs.com\/site\/wp-json\/wp\/v2\/pages\/578\/revisions"}],"predecessor-version":[{"id":625,"href":"https:\/\/www.digifoxlabs.com\/site\/wp-json\/wp\/v2\/pages\/578\/revisions\/625"}],"wp:attachment":[{"href":"https:\/\/www.digifoxlabs.com\/site\/wp-json\/wp\/v2\/media?parent=578"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}