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Machine Learning

5 MUST HAVE SKILLS TO BECOME                        MACHINE LEARNING ENGINEER

First lets understand what MACHINE LEARNING is.
In simple words MACHINE LEARNING is all about making the COMPUTERS to perform intelligent tasks without explicitly coding.
This achieved by training the COMPUTER  with lots of DATA.
For Example : 
Detecting whether a Mail is spam or not.
Recognizing hand written digits.
Fraud detecting in Transactions and many such applications .....

TOP 5 skills to get a MACHINE LEARNING job

  1. MATH SKILLS -  Probability and Statistics, Linear Algebra and Calculus
                        PROBABILITY AND STATISTICS 
                         MACHINE LEARNING is very much closely related to Statistics.
      
                       We need to know The Fundamentals of Statistics and Probability Theory, Descriptive Statistics,  BAYE's Rules and Random Variables, Probability Distributions, Sampling, Hypothesis Testing, Regression and Decision Analysis
                         LINEAR ALGEBRA
                     We need to know working with MATRICES and some basic operations such as Matrix addition and subtraction, Scalar and Vector Multiplication, Inverse, Transpose and Vector Spaces.
                         CALCULUS
                             Basics of  Differential and Integral Calculus
      2. PROGRAMMING SKILLS
           A little bit of programming skills in C++, Java, Python is enough. But its preferred to have the             knowledge of DATA STRUCTURES, ALGORITHMS and OOPS Concepts.
                   Types of DATA STRUCTURES
                   1. Primitive Data Structures - Integer, Float, Character, Boolean
                   2. Non-Primitive Data Structures
                            2.1. Linear Data Structures
                                     2.1.1. Array
                                     2.1.2. Linked List
                                     2.1.3. Stack
                                     2.1.4. Queue 
                            2.2. Non-Linear Data Structures
                                     2.2.1. Graph
                                     2.2.2. Tree


                   OOPS Concepts
                   1. Classes
                   2. Data Abstraction
                   3. Data Encapsulation
                   4. Inheritance
                   5. Polymorphism
                   6. Information Hiding
                   Some of the popular languages to learn for MACHINE LEARNING are Python, R Programming.
      
   3.  DATA ENGINEER SKILLS      
        Ability to work with large amount of Data (BIG DATA), Data Processing, Knowledge of SQL and NOSQL,  ETL (Extract Transform Load) operations, Data Analysis and Visualization Skills

   4. KNOWLEDGE OF MACHINE LEARNING ALGORITHMS
        Should be familiar with popular MACHINE LEARNING Algorithms such as Linear Regression, Logistics Regression, Decision Trees, Random Forest, Clustering (K Means, Hierarchical), Reinforcement Learning and Neural Networks.

   5. KNOWLEDGE OF MACHINE LEARNING FRAMEWORKS
       Familiar with popular Machine Learning Frameworks such as SCIKIT LEARN, TENSORFLOW, AZURE, CAFFE, THEANO, SPARK and TORCH
  
      
   

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