Machine Learning with Python

Online Live | 10 Hours | FDP Certification
499.00 (Inc. GST)

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Learn the concept, syntax, semantics and gain introductory knowledge in Machine Learning principles through examples that provide useful exposure to the various concepts of Machine Learning.

Prerequisites: The tutorial is suitable for absolute beginners.

Utilities: Python, IDE-( PyCharm or Jupyter or Spider), Anaconda

Duration : 10 hours

Batch Date: 14th  Oct’24  to  19th  Oct’24

 

Curriculum

Session-1 : Machine Learning-Introduction

  • Introduction of Machine Learning
  • Evolution of Machine Learning
  • Application of Machine Learning

Session-2 : Machine Learning-Fundamental

  • Difference between Traditional Programming and ML Programming
  • Requirements for Machine Learning Practical Implementation
  • Required software and tools for Machine Learning Implementation
  • Setup Anaconda
  • Installation of Pycharm
  • Configure Pycharm with Anaconda

Session-3 : Python-Basics

  • Introduction of Python
  • Features of Python
  • Working with Python
  • Basic Syntax of Python

Session-4 : Core Concepts of Python (Required for ML Practical Implementation )

  • Loop
  • List
  • Tuple
  • Dictionary
  • String

Session-5 : Steps of Machine Learning Implementations

  • Types of Machine Learning
  • Labelled Data and UnLabelled Data
  • Concept of Supervised Machine Learning
  • Concept of UnSupervised Machine Learning
  • Steps of Machine Learning
  • Concept of Collecting the historic training Data for ML
  • Concept of Preprocess data for ML
  • Concept of Train the model
  • Concept of Test the Algorithm and use it

Session-6 : Data Collection for Machine Learning

  • Types of Data collection- Offline Data and Online Data
  • Practical Implementation of Reading the offline dataset using Numpy
  • Regression and Classification
  • Linear Regression and Logistic Regression

Session-7 : Data Visualization for Machine Learning using Matplotlib

  • Concept of Data Visualization and matplotlib
  • Plotting Lines to represent the data for Machine Learning
  • Plotting customized Lines for data representations
  • Plotting scatter plots using matplotlib
  • Plotting Stackplots using matplotlib
  • Plotting Pie plots and etc using matplotlib

Session-8 : Practical implementation of Supervised Machine Learning Algorithm

  • Implementation of Supervised Machine Learning Algorithms
  • Practical Implementation of Machine Learning Supervised Algorithms- Linear Regression , Logistic Regression, Concept of Sigmoid Function

Session-9 : Practical implementation of Unsupervised Machine Learning Algorithm

  • Concepts and Steps of Unsupervised Machine Learning Algorithm and Clustering
  • Practical Implementation of Machine Learning UnSupervised Algorithms- K-Means Clustering.

 

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