Machine Learning with Python

Become a Expert & Specialization in Machine Learning with Python
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About 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.

Number of hours
Who Need This Training?

Fresher’s / Experienced / Diploma / Graduate /Post-Graduate in any Stream.

Course Duration

450 Hrs (2hrs/Day 12 Months, 4hrs/Day 6 Months or 8hrs/Day 3 Months).

Course Fee Details

₹ 25,000 /-

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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