Machine Learning Online Training

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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