Machine Learning, AI, & Deep Learning

Machine Learning & Analytics

Course Description

Machine learning methods are used for data analysis, which is similar to data mining, but the main goal of machine learning is to automate decision models. Algorithms are the heart and soul of machine learning, and they help computers find hidden insights.

In essence, machine learning algorithms need to learn. The machine needs to learn from data. Data will have multiple dimensions: Type (quantitative or qualitative), amount (big or small), and number of variables available to solve a problem. Learning algorithms should also be as general purpose as possible. We should be looking for algorithms that can be easily applied to a broad class of learning problems.

Data scientists are responsible for machine learning and getting outputs, but business people are the ones who are going to use it for a business purpose, so the rules and insights extracted from machine learning should be interpretable. The output produced by the machine must be understood by humans, who may not be from the machine learning area.

60 Hours

The training aims at providing participants with the latest and general-purpose machine learning algorithms. At the same time, the training aims to deliver some common threads, or a common knowledge base, which can be used in the future for learning a wide range of algorithms.

  • Financial Industry- Participants would learn “Credit Scoring.”
  • Health care industry- Use tensor flow for extracting information.
  • The Canada police differential treatment story.
  • How to find correct weights for food items and have a winning compensation program.
  • Predicting customer churn in a telecom industry.
  • How to reduce a large number of variables
  • Predicting a heart attack
  • Convert categorical information to continuous information

Introduction to Machine Learning

  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories

Overview of Artificial Intelligence

  • What is AI
  • Applications of AI
  • History of AI
  • Inductive Reasoning & Deductive Reasoning
  • What all are included in AI – Robotics, Agent etc

Introduction to R Programming

  • Installation & Setup – R & R Studio
  • Fundamentals: Vector, Function, Packages
  • Matrices: Building, Naming Dimensions, operations, visualizing, sub setting
  • Data Frames: Building, Merging, Visualizing (ggplot2)
  • Hands-on/ Lab

Data Extraction, Wrangling & Exploration

  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data
  • Loading different types of dataset in R
  • Arranging the data
  • Plotting the graphs
  • Hands-on/ Lab

Supervised & Unsupervised Learning

  • Supervised & Un-supervised Learning


  • Simple Linear Regression
  • Multiple Linear Regression
  • Support Vector Machine
  • Hands-on/ Lab


  • Classification
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • What is Navies Bayes?
  • Support Vector Machine: Classification
  • Hands-on/ Lab


  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • Hands-on/ Lab

Overview of Dimensional Reduction

  • Feature Extraction with PCA
  • Feature Selection techniques

Recommendation Engine

  • What are Association Rules & their use cases?
  • What is Recommendation Engine & it’s working?
  • Types of Recommendation Types
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation
  • Recommendation Use-case
  • Hands-on/ Lab

Time Series

  • What is Time Series data?
  • Time Series variables
  • Different components of Time Series data
  • Visualize the data to identify Time Series Components
  • Implement ARIMA model for forecasting
  • Exponential smoothing models
  • Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  • Implement respective ETS model for forecasting
  • Hands-on/ Lab

Overview of Deep Learning

  • What is Deep Learning
  • Biological Neural Networks
  • Understand Artificial Neural Networks
  • Building an Artificial Neural Network
  • How ANN works
  • Important Terminologies of ANN

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