Data Science & Machine Learning with Python

Become a Expert & Specialization in Machine Learning with Python

Duration: 120 Hrs

100% Placement Assistance | 1000+ Hiring Partners
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1977 Learners Enrolled

Program Fees

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₹25000.00

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Talk to our experts 24/7 on 9844441203

About Data Science & Machine Learning with Python

Machine learning is becoming over the modern data-driven world and it is a growing technology among many companies to extensively support many fields, such as search engines, robotics, self-driving cars, and so on. Here in this course you can explore various real-time scenarios where you can use machine learning. This course will be making you to understand and implement all Machine learning algorithms with exciting examples. Every trainer will be delivering machine learning algorithms followed by practical session. Most importantly you will be learning from trainers Bottom level to Top level all algorithms, such as Linear Regression, Logistic Regression, Support Vector Machines, Principal Component Analysis, Time Series Analysis Deep Neural Networks, and so on. This is going to friendly to Python programmers and data analyzers to play around with the code to implement machine learning techniques.

Key Features of Data Science & Machine Learning with Python

Core Java Certification Course
120 Hrs of LIVE online training
Full Stack Web Development Certification Course
LIVE mentoring & Doubt clarification sessions
Cisco CCNA Certification Course
100+ lab assignments
Core Java Certification Course
25 Hrs aptitude and logical reasoning
Full Stack Web Development Certification Course
Interview preparation & Placement assistance

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Drop an email on refund@rooman.net within 7 days of the batch start date.
Data Science with Machine Learning & Python

Course Outline

  • The Print Statement
  • Comments
  • Python Data Structures & Data Types
  • String Operations in Python
  • Simple Input & Output
  • Simple Output Formatting
  • The IF Statement and it’s Related Statements
  • And Example with IF and it’s Related Statements
  • The While Loop
  • The For Loop
  • The Range Statement
  • Break & Continue
  • Assert
  • Examples for Looping
  • Create your own Functions
  • Functions Parameters
  • Variable Arguments
  • Scope of A Function
  • Function Documents/Docstrings
  • Lambda Functions & Map
  • An Exercise with Functions
  • Create A Module
  • Standard Modules
  • Errors
  • Exception Handling with Try
  • Handling Multiple Exceptions
  • Writing your Own Exceptions
  • File Handling Modes
  • Reading Files
  • Writing & Appending to Files
  • Handling File Exceptions
  • The WITH Statement
  • New Style Classes
  • Creating Classes
  • Instance Methods
  • Inheritance
  • Polymorphism
  • Exception Classes & Custom Exceptions
  • Import Libraries
  • Load Dataset
  • Dimensions of the Dataset
  • Peek at the Data
  • Statistical Summary
  • Class Distribution
  • Data Visualization
  • Univariate Plots
  • Multivariate Plots
  • Simple Plots
  • Standard Time Plot
  • Plots with Different Strokes
  • Coloured Plot
  • Another Coloured Plot
  • Dotted Plot
  • Curve and Point
  • Bar Plot
  • Multi-Coloured Plot
  • Polar Plot
  • 2D Data Plot
  • 3D Bar Graph
  • Classification of Linear Regression
  • Implementing Linear Regression
  • Classification of Logistic Regression
  • Implementing Logistic Regression
  • Classification of Naive Bayes
  • Implementing Naive Bayes
  • Introduction to Predictive Modeling
  • Understanding the Support Vector Machines (SVM’s)
  • Using SVM’s
  • Introduction to Clustering
  • Introduction to Unsupervised Learning
  • Using the K-Means Algorithm
  • Evaluating the Performance of Clustering Algorithms
  • Using DBScan Algorithm
  • Introduction to Principal Component Analysis (PCA)
  • Implementing the Principal Components with Clusters
  • Understanding the Concept of Nearest Neighbours Algorithm
  • Implement K-Nearest Neighbours
  • Introduction to Text Data Analyzing
  • Preprocessing Data using Tokenization
  • Implementing Text Analysis
  • Introduction to Speech Recognition
  • Reading and Plotting Audio Data
  • Introduction to Time Series Analysis
  • Slicing Time Series Data
  • Operating on Time Series Data
  • Understanding the Components and Structure of Artificial Neural Networks
  • Understanding and Implementing a Perceptron
  • Implementing a Single Layer Neural Network
  • Implementing a Deep Neural Network
  • Creating a Vector Quantizer
  • Describing the Recurrent Neural Network for Sequential Data Analysis

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At Rooman, we value the trust of our students immensely. If you feel that a course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
To claim a refund, kindly follow the below procedure:

  1. Drop an email on refund@rooman.net with a subject “Online course refund | Course name”. (Please do not forget to send it from the registered email id)
  2. Give a valid reason for the refund. [for our internal purpose only]
  3. Ensure that the email is received within seven days of batch start date. [Example: if batch starts on 28 th Oct’20, you should send the refund email on or before 04th Nov’20 midnight]
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