Data science using Python

Course Highlights

  • 25+ Unique Tasks
  • 10+ Real Business Scenarios
  • Industry used top Algorithms
  • Individual Guidance
  • Placement Guidance

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    Course Description

    Learn to work with Data Science from scratch by using Python, Statistics, Data processing, Supervised Learning Algorithms, Unsupervised Learning, Linear & Logistic Regression, Decision Trees.

    A machine learning engineer creates and teaches machines, programs, and other computer-based systems to make predictions using their learned information. Python's ability to work with data automation and algorithms makes it an excellent programming language for machine learning.

    Course Details

    • Prerequisites: Python Basics
    • Training Mode : Offline / Online / Onsite
    • Introduction To Python
    • Conditional Statements And Loops
    • Pandas And Numpy
    • Data Preprocessing
    • Standardization,Normalization
    • Matplotlib And Seaborn – Intro
    • Line, Bar, Stacked Bar Graphs,  Box Plot
    • Libraries exposed like Pandas and Numpy
    • Scatter Plot, Heat /Map, Histogram, Stacked Histograms
    • Use data visualizations &  statistical plots
    • Introduction to Supervised Ml
    • Classifications Vs Regression
    • Linear Regression
    • Polynomial Regression
    • Logistic Regression
    • Random Forest
    • Decision Tree
    • Introduction to Unsupervised Ml
    • Clustering
    • K-Means Clustering
    • Elbow-Method

    Course Details

    • Hands On Training : 20-30 Hrs
    • Practical Session : 10-20 Hrs
    • Practical Assignment : 50 Hrs

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