Certification in Machine Learning & AI​

Duration: 12 weeks

Self paced program

Course Goals
  • Learn or refresh the basic concepts of programming & practice programming fundamentals in Python
  • Data Science
  • Machine Learning Introduction
  • Math behind ML
  • Terminologies and Algorithms

Certification in Machine Learning & AI

Who should join?

If you are interested in learning the fundamentals of programming through a self paced course, you should sign up for this program.

Course Content
  • Programming fundamentals
  • Practicing in Python
  • Data structures
  • Object Oriented Programming
  • Sending Emails with Python
  • Introduction to using APIs
  • Muti Threading & into to Async programming
  • Introduction to Jupyter Notebooks & Google Collaboratory
  • Data & File Handling
    • Working with .csv | Tabular Data | pools of data
    • Accessing, Reading Writing, Re-writing
    • Loading in runtime with variables
  • Data Operations
    • Introduction to Numpy, Pandas
    • Sort
    • Search
    • Filter
    • Restructuring
    • Re-indexing
  • Data Visualisation
    • Introduction to Plotly and matplotlib
      • Graph & Plots
      • Bar Chart
      • Pie Chart
      • Heat Map
      • Histogram
      • Scatter plot
  • Introduction to Machine Learning
  • Learning of Features – Machine Learning
  • ML vs traditional conditional programming
  • Machine Learning terminology
    • Classification
    • Regression
    • Clustering
  • Types of Machine Learning
    • Supervised
    • Un-Supervised
    • Semi-Supervised
    • Reinforcement Learning
    • Ensemble Learning- Multiple ML algorithms
  • Machine Learning Prcecess
    • Thinking like a ML scientist
  • Datasets
    • Data Sources- how to get data
  • Features
    • Identifying revant features
    • Preparing data
      • Feature extraction
      • Normalisation
      • Batch standardisation
Data Augmentation
  • Machine Learning Models
    • Model Types
      • Classical ML
        • Regression
          • Linear
          • Logistic
        • Random forest
        • Decision Tree
        • Markov Model
      • Deep Learning
        • Perceptron
        • Computer Vision
          • Layers
            • Convolution Layers
            • Pooling Layers and types
            • Flatten Layers
            • Dense/fully connected Layers Natural Language
            • Vector Maps
            • Work Ecodings
            • Language models
              • Transformers
  • Carried out through
    • Assignments
    • Quizzes
    • Projects

    Choose your mode of learning:

     

     
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