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ML Programming

Course Highlights

  • 25+ Scenarios
  • 10+ Real world Use cases
  • Understanding Techniques from Scratch
  • Individual Guidance
  • Unique Training & Internship Program (TIP) & Evaluation

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    Course Description

    Automation is the in-demand requirement of digital transformation. AI and machine learning engineers are expected to be in high demand. Experience in the field is highly challenging and requires the individual to work on ‘n’ number of algorithms. Any aspiring machine learning engineer should be confident in their skills of mathematics & computer science. You should be able to recognize the inefficiencies of each algorithm and recommend the better ones. 

    Most of the freshers are tempted to move to Artificial Intelligence, since the market promises very high paid jobs. To get into ML you must be strong in Logical & mathematical skills and should show deep interest in research and analysis. Minimum of two to three years of experience in relevant industry will fetch you the status of ML Engineer. The average salary of an ML Engineer with a minimum of 2+ years experience is 5.4 LPA. 

    Course Details

    • Assignment: 1 per session
    • Assessment & Evaluations: 1 per week
    • Training Mode : Offline / Online / Onsite
    Tools Covered
    • Matplotlib, Seaborn
    • Pandas, Numpy
    • Histogram
    • TensorFlow, NLTK
    Course Content
    • Python Basics
    • Conditional Statements And Loops
    • Pandas And Numpy
    • Data Preprocessing
    • Data Visualization
    • Line, Bar, Stacked Bar Graphs,  Box Plot
    • Libraries Exposed Like Pandas And Numpy
    • Scatter Plot, Heat /Map, Histogram, Stacked Histograms
    • Supervised ML Algorithms
      • Classifications Vs Regression
      • Linear Regression
      • Polynomial Regression
      • Logistic Regression
      • Random Forest
      • Decision Tree
    • Unsupervised ML Algorithms
      • Clustering
      • K-Means Clustering
      • Elbow-Method

    Course Details

    • Hands On Training : 20-30 Hrs
    • Practical Session : 10-20 Hrs
    • Practical Assignment : 1 per session

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