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Machine Learning with Cloud Automation

Why Machine Learning?

A subset of artificial intelligence (AI), machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction. Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. If any corrections are identified, the algorithm can incorporate that information to improve its future decision making.

Machine Learning Industrial Exposure

Machine learning has applications in all types of industries, including manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services, and energy, feedstock, and utilities.

  • Predictive maintenance and condition monitoring
  • Upselling and cross-channel marketing
  • Healthcare and life sciences. Disease identification and risk satisfaction
  • Travel and hospitality. Dynamic pricing.
  • Cloud Solution and Analysis.
  • Financial services. Risk analytics and regulation
  • Energy. Energy demand and supply optimization

Prerequisite: No Prerequisite. However knowledge of Python programming language will be added advantage.

System Requirements: 4GB RAM along with Windows 10 and Python 3.x

Machine Learning with Cloud Automation Course Curriculum

Course Duration: 4/6 Weeks & 3/6 Months

Online : Regular Batches / Weekend Batches

Live Project


Soft Skill Development

Advanced Programs

  • What is Machine learning?
  • The three different types of machine learning
  • Supervised, unsupervised, reinforcement learning
  • An introduction to the basic terminology and notations
  • Configuration and Environment Setup
  • Visualization Tools like Seaborn, Matplotlib
  • Introduction to Python language
  • Environmental setup for Python, Anaconda, Jupyter, Spider etc
  • Data types of Python, numbers, string
  • Run a python program in jupyter/spider/ipython
  • If, elseif, Loops in python
  • Functions in python
  • Positional argument, keyword argument, default argument
  • Lambda function
  • Module of Python
  • Build in and user defined module
  • Import a module, call a function of a module.
  • Introduction to Object oriented language
  • Class, object
  • Create class and object in python
  • Creating and accessing strings
  • Indexing and slicing on string
  • Strings methods
  • List and its methods
  • Accessing lists
  • Tuple, set, dictionary and their methods
  • Indexing, slicing on list, tuple, set , dictionary
  • List comprehension and its uses
  • Basic Statistics – Measures of Central Tendencies and Variance, Mean Median Mode
  • Conditional  Probability
  • Introduction to Numpy
  • Creating 1D and 2D Numpy array
  • Slicing and indexing of Numpy Array
  • Operations on Numpy array
  • Introduction to Pandas
  • Series and DataFrame
  • Operations on series and dataframe
  • Using pandas plotting functions
  • Introduction to Matplotlib and Seaborn
  • Difference between the two
  • Scatter plot using matplotlib
  • Draw histogram, barchart, pie chart to any data
  • Explain supervised
  • Difference between classification and regression
  • Single and Multiple Linear Regression
  • Mathematics behind linear regression Simple linear regression with example
  • Logistic regression to solve classification problem
  • Apply logistic regression on titanic dataset
  • Classification report and confusion matrix
  • What is unsupervised learning
  • Types of unsupervised learning
  • Concepts of clustering
  • K means clustering to cluster group of data
  • Introduction to Google Colab Cloud
  • GPU and CPU concept
  • Deployment Machine Learning model to Colab
  • Introduction to EC2 Instance on Windows/Linux.
  • Introduction to DevOps
  • Git Architecture
  • Concept
  • Implementation

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