Machine learning

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Machine Learning Course Overview

In this course you will learn all the concepts of Python and ML along with Supervised and unsupervised learning. Whether you are fresher or experienced in programming, this course will put you on the quick track to honing your Machine Learning using python skills. In this course you’ll get hands-on experience in Python that you’ll be able to immediately apply in the real world. The course will cover the basics of Python and quite a lot of tools used for Machine Learning.

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Course Fees: Rs.12000

  • Duration : 2 months
  • No Cost EMI : INR 6000 x 2 month

NEFT Payment : Bank account details: Account Name – KLMS Hands-On Systems Private Limited, Account No – 50200042627525, IFSC – HDFC000027

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Course Description!

Machine Learning Course Curriculum

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Course content

  • Introduction to Machine Learning
  • Types of Machine learning
  • Why data science and machine learning is future
  • Different between Data Science Big data and data analysis
  • Data understanding: real life example
  • Why data science and machine learning is future
  • Analytics vs. Data warehousing
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • Process of model creation
  • Which skills are required for Machine learning?
  • Introduction of python
  • History of Python
  • Why python is so famous
  • Installation of Python and Anaconda
  • Compiler & Interpreter
  • Variable, Keywords
  • Comments & Indentation
  • Write your first program
  • Data types, Input and output function
  • Arithmetic operator, Relational Operator
  • Assignment Operator
  • Logical operator, Bitwise operator
  • Membership Operator, Identity Operator
  • If statement, if-else, if-elif-else, Nested if else
  • While loop, For loop
  • Nested while loop, Nested for loop
  • Break, Continue and Pass
  • Basics Defining function
  • Function call Return statement
  • Function with parameter and without parameter
  • local and global variable
  • Recursion, Anonymous (lambda) function
  • User define functions
  • Numeric type basics
  • Hexadecimal, Octal and Binary Notation
  • Complex Numbers, Type casting Numeric Functions
  • Random number generation(Using Random Modules)
  • Defining a string
  • Different ways to create string
  • Escape sequence, Raw string String methods
  • String formatting Expressions
  • Assignments
  • Defining & Creating list, Accessing list elements of list
  • List methods, Functions used with list
  • Implementation of stack and queue using list
  • Defining & Creating a tuple
  • Accessing elements of tuple, What is Immutability
  • Tuple Methods, Functions used with tuple
  • Defining & Creating a dictionary
  • Accessing elements of dictionary
  • Dictionary methods & Functions
  • Defining & Creating set
  • Set operations
  • Defining module, Importing module
  • Dir(), Module search path, Sys module, Os module
  • Namespace
  • Defining and create package
  • Installing third party packages
  • Assignments
  • Data science libraries: Numpy, pandas, Matplotlib etc
  • Array, Array indexing, Array operation
  • Data frame, series, Groupby, missing values
  • Data input and output
  • Analysis exercise
  • Chart prepration
  • Box plot, Scatter plot, Chart styling, Histogram, Bar chart etc.
  • Groupby ploting
  • Data visualization exercise
  • Correlation, Mean square error
  • R- Squared
  • Linear Regression Technique
  • Polynomial Regression
  • Multiple- Linear Regression
  • Processing CSV data
  • Correlation
  • Data cleaning techniques
  • Confusion Matrix, ROC & AUC Curve
  • Type-1 and Type-2 Error
  • Precesion & Recall
  • What is Logistic Regression
  • Concept and theory
  • Sigmoid function & Mathematical approach
  • K-Nearest Neighbors
  • Concept and theory
  • . Mathematical approach, Distance functions: Euclidean, Minkowski
  • Support Vector machine
  • Introduction to Support Vector machine
  • Mathematical Approach
  • Theory on hyperplane
  • Dataset with problem description
  • Practical application on Python
  • Significance of using Decision Tree
  • Different kinds of Decision Tree
  • Procedure and technique of Decision Tree
  • Random Forest
  • Theory and mathematical concepts
  • Entropy and Decision Tree
  • Dataset with problem description
  • Classification & Regression using random forest on Python
  • Introduction of Naïve Bayes
  • Theory of classification
  • Concept of probability: prior and posterior
  • Bayes Theorem
  • Mathematical concepts
  • Limitation of Naïve Bayes
  • Dataset with problem description
  • Practical application on Python
  • Introduction of clustering
  • K-mean clustering
  • Hierachical clustering
  • Dataset with problem description
  • Practical application on Python
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Why to choose Handson?

Hands-On Training

Using a hands-on approach to training, participants will adopt the form of learning that typically benefit from the most. It allows people to learn by doing.

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Removes travel expenses, Save money – No hotels, no rental cars, or meal costs, Save time – Flexible schedule allows you to stay in touch with the plant, Easy to use virtual classroom, Each class is recorded, review as much as you like.

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Need training just for up-skilling your team? We can do that. Live online training is ideal to train staff located anywhere in the world. The training can also be customized to your needs.

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Join the Virtual Internship

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