Machine Learning in Mumbai – ICSS

Machine Learning training in Mumbai

Machine learning training in Mumbai using Python from data scientists with practical course modules from Indian Cyber Security Solutions. In-depth Machine Learning course in Mumbai designed for beginners. ICSS rated among the best Machine Learning Training Institute in Mumbai.

Machine Learning Training in Mumbai by Indian Cyber Security Solutions is designed to provide a broad introduction to machine learning. Machine learning course in Mumbai is one of the highly demanded courses. More than 130+ students got placed in varied industries, Are you searching for “Best Machine Learning training near me”? Join Indian Cyber Security Solutions which is the Best Machine Learning Training institute in Mumbai. ICSS is considered among the top training institute providing the best machine learning training in Mumbai.

By leveraging Machine Learning Training you will get exposure to numerous high-paying job opportunities. Machine learning Course in Hyderabad is so pervasive today that you probably use it tons of times a day without even knowing it.

The ML & AI course goes in-depth into the techniques used by Data scientists and demonstrates it live in a lab-based 100% practically oriented class. Many researchers also think it is the best way to make progress towards human-level AI. You will learn about the most effective machine learning techniques, and gain expertise by implementing them and getting them to work for yourself. Get yourself enrolled in the most technical and practical sessions and learn the art of machine learning by joining the machine learning training in Hyderabad conducted by ICSS.

Machine Learning Training in Mumbai

Machine Learning Course in Mumbai

Machine Learning Course in Mumbai by ICSS will master you in algorithms like regression, clustering & classification. ML is an area of artificial intelligence and computer science that includes the development of software and algorithms. It can make predictions based on data. The software can make decisions and follow a path that is not specifically programmed. Machine learning is used within the field of data analytics to make predictions based on trends and insights in the data. A prime example of the application of machine learning is the autonomous vehicle. Sensors around the vehicle deliver thousands of data points that are analyzed and processed to move the vehicle toward its destination. Collective data from thousands of self-driving cars can be used to improve vehicle safety and prevent accidents. Gain perfect knowledge about Machine Learning from the machine learning course in Mumbai from ICSS to establish yourself in this field.

In this Machine Learning Course in Mumbai, you will gain a comprehensive understanding of how machine learning and artificial intelligence works, to bring a technical perspective to the workplace. In this course, you can learn how to use real data and select the relevant machine learning model to create a project, and learn how to leverage these frameworks and tools to make decisions. After completion of the machine learning training in Mumbai, you will earn a certificate, which can boost your LinkedIn profile and resume, helping you stand out in the job market. Gain confidence with supervised and unsupervised learning, regression, classification, clustering methods, and learn about neural networks, one of the most popular methods in the industry.

 

Course Curriculum

 

Module 1: Features of Python

Using the Python Interpreter

  • Invoking the Interpreter
  • The Interpreter and Its Environment

Introduction to Anaconda

  • Anaconda Navigator
  • Anaconda Prompt
  • Python Console
  • Jupyter qt-console
  • Jupyter Notebook
  • Spyder

An Informal Introduction to Python

  • Using Python as a Calculator
  • First Steps Towards Programming

 

Module 2: Data Structures

  • Lists
  • The del statement
  • Tuples and Sequences
  • Sets
  • Dictionaries
  • Looping Techniques
  • More on Conditions

Module 3: Concept of Modules

  • Standard Modules
  • The dir() Function
  • Packages Input and Output Reading and Writing Files

Module 4: OOPS Concept

  • Object: properties and operations
  • Class as a blueprint for objects
  • Fields: Python convention for defining private fields
  • Constructors: overloading and chaining
  • Designing static properties, operations, and blocks in Python
  • Special method names in a class and their uses (e.g. __new__, __del__, __str__ etc.)
  • Single and multiple inheritances
  • Method overriding and polymorphism
  • Iterators
  • Errors and Exceptions
  • Syntax Errors
  • Exceptions
  • Handling Exceptions
  • Raising Exceptions

Input & Output

  • Fancier Output Formatting
  • Reading and Writing Files

Installation to Anaconda and Control Flow Tools

  • if Statements
  • for Statements
  • The range() Function
  • break and continue Statements, and else Clauses on Loops
  • pass Statements
  • Defining Functions
  • More on Defining Function
  • Function special attributes
  • Coding Style

 

Module 5:

  • Single and multiple inheritances
  • Method overriding and polymorphism
  • Iterators
  • Generators
  • Generator Expressions
  • Errors and Exceptions
  • Syntax Errors
  • Exceptions
  • Handling Exceptions
  • Raising Exceptions
  • User-defined Exceptions
  • Defining Clean-up Actions
  • Predefined Clean-up Actions

Module 6: Numpy

  • An example
  • Array Creation
  • Printing Arrays
  • Basic Operations
  • Universal Functions
  • Indexing, Slicing, and Iterating

Shape Manipulation

  • Changing the shape of an array
  • Stacking together different arrays
  • Splitting one array into several smaller ones

Copies and Views

  • No Copy at All
  • View or Shallow Copy
  • Deep Copy
  • Functions and Methods Overview

Broadcasting rules Fancy indexing and index tricks

  • Indexing with Arrays of Indices
  • Indexing with Boolean Arrays
  • The ix_() function
  • Indexing with strings

Linear Algebra

  • Simple Array Operations

Tricks and Tips

  • “Automatic” Reshaping
  • Vector Stacking
  • Histograms

Module 7: Basic of Pandas

Introduction to Pandas Data Structures

  • Series
  • DataFrame
  • Index Objects Reindexing
  • Dropping entries from an axis
  • Indexing, selection, and filtering
  • Arithmetic and data alignment
  • Function application and mapping
  • Sorting and ranking
  • Axis indexes and duplicate values Filtering out missing Data
  • Filling out missing Data

Essential Functionality

  • Reindexing
  • Dropping entries from an axis
  • Indexing, selection, and filtering
  • Arithmetic and data alignment
  • Function application and mapping
  • Sorting and ranking
  • Axis indexes and duplicate values

Summarizing and Computing descriptive statistics

  • Correlation and Covariance
  • Unique values, Value Counts and Membership

Handling missing data

  • Filtering out missing Data
  • Filling out missing Data

Hierarchical Indexing

  • Reordering and sorting levels
  • Summary statistics by level
  • Using a data frame’s column

Other Pandas topics

  • Integer indexing
  • Panel Data

Module 8: Data Visualization with Matplotlib & Seaborn

Module 9: Supervised Learning 1
  • K-Nearest Neighbours
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees
  • Project-1 on data analysis with documentation

Module 10: Machine Learning vs Statistical Modelling

  • Machine Learning Languages, Types, and Examples
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning

Supervised Learning II

  • Regression Algorithm (Linear, Multiple, Polynomial)
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models

 

Module 11: Unsupervised Learning

  • K-Means Clustering plus Advantages & Disadvantages

Module 12: Logistic Regression Algorithm

Module 13: Practice Linear & Logistic Regression with Different Dataset

Module 14: K Means & Hierarchical Clustering Algorithm 

Module 15: PCA & Decision Tree Algorithm
Module 16: Random Forest Algorithm
Module 17: K-Nearest Neighbor Algorithm
Module 18: Support Vector Machine & Natural Language Processing with Naive Bayes
Doubt Clear Session
Final Project with Documentation

Machine Learning Training Institute in Mumbai

Best Machine Learning Training Institute in Mumbai which provides basic to advance the level concept of Data Science. ML is a method of data analysis that automates analytical model building. Moreover, it’s a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

Because of new computing technologies, today’s machine learning is not like the past. You will be able to expose new data and independently adapt after Machine Learning Training in Mumbai. They learn from previous computations to produce reliable, repeatable decisions and results. In conclusion, it’s a science that’s not new – but one that has gained fresh momentum. Many students have placed according to their skills that make us preferred best Machine Learning Institute in Mumbai. In this course, you will learn about the most effective machine learning techniques from ICSS which is considered to be the best Machine Learning Training Institute in Mumbai as per ABP News.