Career Path1034 Ratings

Machine Learning is one of the most in-demand skill as no one wants to go for rule-based AI tools any more i.e machine that decides what to do on the bases of what we have preset. The problem with that approach is that we can not build any model which is to complex for example facial recognition and that is just one issue that you would face, predefining each possible step is so much time consuming but in machine learning you teach a machine to do work as per your requirements and when machine face a new situation it react as per what it has learned in past for example:
A=2 B=4
A=3 B=9
A=4 B=16
A=5 B=??
Our machine algorithm will solve this for us, is that great, think about it it can go as long as it wants to it will solve all.
Remember machine learning is not errored free but still, it's far more capable, that we are ready to risk that.

Who can choose this career

Professionals, Students or anyone who has an interest in Machine Learning algorithm.

Requirements

One must know algorithms and math basic to learn machine learning. Having a decent knowledge of python can be a blessing.

Learning Path

Course 1

Statistics For Data Science

The statistic is a beautiful field which plays under the huge tree of calculation known as Mathematics. Statistics aka "STATS" is a branch that plays around data collection, organization, analysis, interpretation and presentation. It is the very tools used worldwide solve any analytical problem from business to the education system, from research to development and yes STATS is a complete deep subject in itself and is the backbone of any analysis and data science.
Course curriculum

Module 1: Statistic

  • 1.1  What Why How Of Stats
  • 1.1  Stats Introduction
  • 1.1  Stats Population And Sample
  • 1.1  Stats Topic We Gonna Cover

Module 2: Data And Stats

  • 2.1  Data Type
  • 2.1  Descriptive Inferential
  • 2.1  Frequency Distribution
  • 2.1  Frequency Distributions Visualization
  • 2.1  Central Tendency
  • 2.1  Standard Deviation
  • 2.1  M.A.D.
  • 2.1  C. O. V.
  • 2.1  Moments

Module 3: Plots And Lines

  • 3.1  Quarantines
  • 3.1  Skewness
  • 3.1  Kurtosis
  • 3.1  Regression Analysis
  • 3.1  Correlation
  • 3.1  Normal Distribution

Module 4: Formulas And Stats

  • 4.1  Euclidean
  • 4.1  Part 1 Manhattan
  • 4.1  Part 2 Malinowski

Module 5: Bonus Videos For You :)

  • 5.1  Anova Bonus Video
  • 5.1  Black Scholes Bonus Video
  • 5.1  Chi-Distribution,Permutation,Combination,Cumulative Frequency Bonus Video
  • 5.1  C.O.V. And C.C. Bonus Video
  • 5.1  Data Collection Bonus Video
  • 5.1  Hypo And Patterns Bonus Video
  • 5.1  Kutosis Bonus Video
  • 5.1  Probability Bonus Video
  • 5.1  Bayesian Theory Bonus Video
  • 5.1  Type Error Bonus Video " Thanks You " Last Bonus Video
Course 2

Power BI

Power BI is a business analytics tool that allow you to visualize all insight of data and make the prediction of the future as per requirement, and all big decisions are made like that in today's world.
Course curriculum

Module 1: Visualization

  • 1.1  Introduction Of Visualization Tool
  • 1.1  Introduction Of Power BI
  • 1.1  Where To Find Power BI

Module 2: Power BI

  • 2.1  Power BI Starts
  • 2.1  Calculated Field
  • 2.1  Visualization
  • 2.1  Visualization Right Way
  • 2.1  Slicer
  • 2.1  Edit Query
  • 2.1  Dax Introduction
  • 2.1  Using Shape
  • 2.1  Mobile Friendly View

Module 3: Dax Language

  • 3.1  Dax Starts Very Important Part
  • 3.1  Dax Starts
  • 3.1  Dax Introduction
  • 3.1  Syntax
  • 3.1  Function
  • 3.1  Filter
  • 3.1  Dax Filters
  • 3.1  Dax Power BI Aggregation Function
  • 3.1  Filter Domain
  • 3.1  Time Intelligence
  • 3.1  Date Time Function.
  • 3.1  Info And Logic Function
  • 3.1  Math Trig Function
  • 3.1  Statistical Function
  • 3.1  Parent,Text,Other Last Video Dax

Module 4: Project Making

  • 4.1  Project
  • 4.1  Project Made
  • 4.1  Project Making

Module 5: Main Projects

  • 5.1  Creative Compare Match Style
  • 5.1  Loss Detection By Dax And Projection
  • 5.1  Sales On Map
  • 5.1  Sales Profit As Per Category and Sub Category

Module 6: Ending Conclusions

  • 6.1  End Conclusion Important Concept
Course 3

Python Basics

This course covers all concepts of python that are required to develop a decent project by tour own and we also cover some extra bonus videos to a complete knowledge of python language.
Course curriculum

Module 1: Introduction

  • 1.1  Why To Learn Python
  • 1.1  Python Introduction
  • 1.1  Resource
  • 1.1  History of Python
  • 1.1  Python Installation
  • 1.1  How Python Works?

Module 2: Get Started

  • 2.1  Python First Program
  • 2.1  Basic Concept
  • 2.1  Hello World
  • 2.1  Python Comments - Single line
  • 2.1  Python Comments - Multi-line
  • 2.1  Variable Introduction
  • 2.1  Python Variables
  • 2.1  Everything is an Object in Python
  • 2.1  Indentation in Python
  • 2.1  Python Data Structure
  • 2.1  Storing Values in Variable
  • 2.1  Type Casting
  • 2.1  Checking Types of Variables - type()
  • 2.1  Type Casting - integer to float and vice versa
  • 2.1  Type Casting - Int to string and vice versa
  • 2.1  Implicit Type Casting
  • 2.1  Explicit Type Casting
  • 2.1  Naming Variables in Python
  • 2.1  Keywords & Identifiers in Python

Module 3: Input From User

  • 3.1  Input from User in Python
  • 3.1  Input Out
  • 3.1  Simple Input Output
  • 3.1  Input On Strings
  • 3.1  Part 2 String IO
  • 3.1  Simple Input Output In Strings
  • 3.1  Example: Taking input in Python
  • 3.1  Type Conversions
  • 3.1  Example: Input in Python - with int and float type-casting

Module 4: Control Structure

  • 4.1  Control Structure
  • 4.1  If Condition
  • 4.1  If Statement Part 2
  • 4.1  If Else
  • 4.1  If Else
  • 4.1  If Elif Else
  • 4.1  If Else Elif
  • 4.1  Nested If
  • 4.1  While
  • 4.1  Code Examples - Conditional Statement
  • 4.1  Example : Simple Calculator

Module 5: Loops And Recursion

  • 5.1  Looping Statements in Python
  • 5.1  For Loop
  • 5.1  How to print name 500 times?
  • 5.1  Example: Python For Loops
  • 5.1  Python Loops - Range (start,stop,step)
  • 5.1  Example: Python Loops - Range (start,stop,step)
  • 5.1  Examples: Python Nested Loops
  • 5.1  For Loop vs While Loops
  • 5.1  Fun With Python
  • 5.1  Break Statement

Module 6: List in Python - Data Structure

  • 6.1  Python List
  • 6.1  Python List Operations
  • 6.1  Python List Function
  • 6.1  Python List Range
  • 6.1  Example Codes - Python Lists
  • 6.1  Python Lists - Delete element from list
  • 6.1  Python Lists - Iterate over a list
  • 6.1  Python Lists - Mixed Data Types
  • 6.1  List Comprehension
  • 6.1  Python Lists - Lists of List
  • 6.1  Python Lists - How to Iterate Nested Lists?

Learning Path

Course 1

Statistics For Data Science

The statistic is a beautiful field which plays under the huge tree of calculation known as Mathematics. Statistics aka "STATS" is a branch that plays around data collection, organization, analysis, interpretation and presentation. It is the very tools used worldwide solve any analytical problem from business to the education system, from research to development and yes STATS is a complete deep subject in itself and is the backbone of any analysis and data science.
Course curriculum

Module 1: Statistic

  • 1.1  What Why How Of Stats
  • 1.1  Stats Introduction
  • 1.1  Stats Population And Sample
  • 1.1  Stats Topic We Gonna Cover

Module 2: Data And Stats

  • 2.1  Data Type
  • 2.1  Descriptive Inferential
  • 2.1  Frequency Distribution
  • 2.1  Frequency Distributions Visualization
  • 2.1  Central Tendency
  • 2.1  Standard Deviation
  • 2.1  M.A.D.
  • 2.1  C. O. V.
  • 2.1  Moments

Module 3: Plots And Lines

  • 3.1  Quarantines
  • 3.1  Skewness
  • 3.1  Kurtosis
  • 3.1  Regression Analysis
  • 3.1  Correlation
  • 3.1  Normal Distribution

Module 4: Formulas And Stats

  • 4.1  Euclidean
  • 4.1  Part 1 Manhattan
  • 4.1  Part 2 Malinowski

Module 5: Bonus Videos For You :)

  • 5.1  Anova Bonus Video
  • 5.1  Black Scholes Bonus Video
  • 5.1  Chi-Distribution,Permutation,Combination,Cumulative Frequency Bonus Video
  • 5.1  C.O.V. And C.C. Bonus Video
  • 5.1  Data Collection Bonus Video
  • 5.1  Hypo And Patterns Bonus Video
  • 5.1  Kutosis Bonus Video
  • 5.1  Probability Bonus Video
  • 5.1  Bayesian Theory Bonus Video
  • 5.1  Type Error Bonus Video " Thanks You " Last Bonus Video
Course 2

Power BI

Power BI is a business analytics tool that allow you to visualize all insight of data and make the prediction of the future as per requirement, and all big decisions are made like that in today's world.
Course curriculum

Module 1: Visualization

  • 1.1  Introduction Of Visualization Tool
  • 1.1  Introduction Of Power BI
  • 1.1  Where To Find Power BI

Module 2: Power BI

  • 2.1  Power BI Starts
  • 2.1  Calculated Field
  • 2.1  Visualization
  • 2.1  Visualization Right Way
  • 2.1  Slicer
  • 2.1  Edit Query
  • 2.1  Dax Introduction
  • 2.1  Using Shape
  • 2.1  Mobile Friendly View

Module 3: Dax Language

  • 3.1  Dax Starts Very Important Part
  • 3.1  Dax Starts
  • 3.1  Dax Introduction
  • 3.1  Syntax
  • 3.1  Function
  • 3.1  Filter
  • 3.1  Dax Filters
  • 3.1  Dax Power BI Aggregation Function
  • 3.1  Filter Domain
  • 3.1  Time Intelligence
  • 3.1  Date Time Function.
  • 3.1  Info And Logic Function
  • 3.1  Math Trig Function
  • 3.1  Statistical Function
  • 3.1  Parent,Text,Other Last Video Dax

Module 4: Project Making

  • 4.1  Project
  • 4.1  Project Made
  • 4.1  Project Making

Module 5: Main Projects

  • 5.1  Creative Compare Match Style
  • 5.1  Loss Detection By Dax And Projection
  • 5.1  Sales On Map
  • 5.1  Sales Profit As Per Category and Sub Category

Module 6: Ending Conclusions

  • 6.1  End Conclusion Important Concept
Course 3

Python Basics

This course covers all concepts of python that are required to develop a decent project by tour own and we also cover some extra bonus videos to a complete knowledge of python language.
Course curriculum

Module 1: Introduction

  • 1.1  Why To Learn Python
  • 1.1  Python Introduction
  • 1.1  Resource
  • 1.1  History of Python
  • 1.1  Python Installation
  • 1.1  How Python Works?

Module 2: Get Started

  • 2.1  Python First Program
  • 2.1  Basic Concept
  • 2.1  Hello World
  • 2.1  Python Comments - Single line
  • 2.1  Python Comments - Multi-line
  • 2.1  Variable Introduction
  • 2.1  Python Variables
  • 2.1  Everything is an Object in Python
  • 2.1  Indentation in Python
  • 2.1  Python Data Structure
  • 2.1  Storing Values in Variable
  • 2.1  Type Casting
  • 2.1  Checking Types of Variables - type()
  • 2.1  Type Casting - integer to float and vice versa
  • 2.1  Type Casting - Int to string and vice versa
  • 2.1  Implicit Type Casting
  • 2.1  Explicit Type Casting
  • 2.1  Naming Variables in Python
  • 2.1  Keywords & Identifiers in Python

Module 3: Input From User

  • 3.1  Input from User in Python
  • 3.1  Input Out
  • 3.1  Simple Input Output
  • 3.1  Input On Strings
  • 3.1  Part 2 String IO
  • 3.1  Simple Input Output In Strings
  • 3.1  Example: Taking input in Python
  • 3.1  Type Conversions
  • 3.1  Example: Input in Python - with int and float type-casting

Module 4: Control Structure

  • 4.1  Control Structure
  • 4.1  If Condition
  • 4.1  If Statement Part 2
  • 4.1  If Else
  • 4.1  If Else
  • 4.1  If Elif Else
  • 4.1  If Else Elif
  • 4.1  Nested If
  • 4.1  While
  • 4.1  Code Examples - Conditional Statement
  • 4.1  Example : Simple Calculator

Module 5: Loops And Recursion

  • 5.1  Looping Statements in Python
  • 5.1  For Loop
  • 5.1  How to print name 500 times?
  • 5.1  Example: Python For Loops
  • 5.1  Python Loops - Range (start,stop,step)
  • 5.1  Example: Python Loops - Range (start,stop,step)
  • 5.1  Examples: Python Nested Loops
  • 5.1  For Loop vs While Loops
  • 5.1  Fun With Python
  • 5.1  Break Statement

Module 6: List in Python - Data Structure

  • 6.1  Python List
  • 6.1  Python List Operations
  • 6.1  Python List Function
  • 6.1  Python List Range
  • 6.1  Example Codes - Python Lists
  • 6.1  Python Lists - Delete element from list
  • 6.1  Python Lists - Iterate over a list
  • 6.1  Python Lists - Mixed Data Types
  • 6.1  List Comprehension
  • 6.1  Python Lists - Lists of List
  • 6.1  Python Lists - How to Iterate Nested Lists?