Machine Learning Online Training

Machine Learning Online Training

0 STUDENTS ENROLLED

    Machine Learning Course Content :

    1.An Introduction to Python

    2.Beginning Python Basics

    • The print statement
    • Comments
    • Python Data Structures & Data Types
    • String Operations in Python
    • Simple Input & Output
    • Simple Output Formatting

    3.Python Program Flow

    • Indentation
    • The If statement and its’ related statement
    • An example with if and it’s related statement
    • The while loop
    • The for loop
    • The range statement
    • Break & Continue
    • Assert
    • Examples for looping

    4.Functions & Modules

    • Create your own functions
    • Functions Parameters
    • Variable Arguments
    • Scope of a Function
    • Function Documentation/Docstrings
    • Lambda Functions & map
    • An Exercise with functions
    • Create a Module
    • Standard Modules

    5.Exceptions

    • Errors
    • Exception Handling with try
    • Handling Multiple Exceptions
    • Writing your own Exceptions

    6.File Handling

    • File Handling Modes
    • Reading Files
    • Writing & Appending to Files
    • Handling File Exceptions
    • The with statement

    7.Classes In Python

    • New Style Classes
    • Creating Classes
    • Instance Methods
    • Inheritance
    • Polymorphism
    • Exception Classes & Custom Exceptions

    8.Regular Expressions

    • Simple Character Matches
    • Special Characters
    • Character Classes
    • Quantifiers
    • The Dot Character
    • Greedy Matches
    • Grouping
    • Matching at Beginning or End
    • Match Objects
    • Substituting
    • Splitting a String
    • Compiling Regular Expressions
    • Flags

    9.Data Structures

    • List Comprehensions
    • Nested List Comprehensions
    • Dictionary Comprehensions
    • Functions
    • Default Parameters
    • Variable Arguments
    • Specialized Sorts
    • Iterators
    • Generators
    • The Functions any and all
    • The with Statement
    • Data Compression

     

    Data Science Course Content:-

    1.Getting Started With Data Science And Recommender Systems

    • Data Science Overview
    • Reasons to use Data Science
    • Project Lifecycle
    • Data Acquirement
    • Evaluation of Input Data
    • Transforming Data
    • Statistical and analytical methods to work with data
    • Machine Learning basics
    • Introduction to Recommender systems

    2.Reasons To Use, Project Lifecycle

    • What is Data Science?
    • What Kind of Problems can you solve?
    • Data Science Project Life Cycle
    • Data Science-Basic Principles
    • Data Acquisition
    • Data Collection
    • Understanding Data- Attributes in a Data, Different types of Variables
    • Build the Variable type Hierarchy

    3.Machine Learning In Data Science

    • Discussion about Box plot and Outlier
    • Goal.Increase Profits of a Store
    • Areas of increasing the efficiency
    • Data Request
    • Business Problem.To maximize shop Profits
    • What are Interlinked variables
    • What is Strategy
    • Interaction b/w the Variables
    • Univariate analysis
    • Multivariate analysis
    • Bivariate analysis
    • Relation b/w Variables
    • Standardize Variables
    • What is Hypothesis?
    • Interpret the Correlation
    • Negative Correlation
    • Machine Learning

    4.Statistical And Analytical Methods Dealing With Data, Implementation Of

    5.Testing And Assessment, Production Deployment And More

    • Multi variable analysis
    • linear regration
    • Simple linear regration
    • Hypothesis testing
    • Speculation vs. claim(Query)
    • Sample
    • Step to test your hypothesis
    • performance measure
    • Generate null hypothesis
    • alternative hypothesis
    • Testing the hypothesis
    • Threshold value
    • Hypothesis testing explanation by example
    • Null Hypothesis
    • Alternative Hypothesis
    • Probability
    • Histogram of mean value
    • Revisit CHI-SQUARE independence test
    • Correlation between Nominal Variable

    6.Business Algorithms, Simple Approaches To Prediction, Building Model, Model Deployment

    • Machine Learning
    • Importance of Algorithms
    • Supervised and Unsupervised Learning
    • Various Algorithms on Business
    • Simple approaches to Prediction
    • Predict Algorithms

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