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Course Overview

From image recognition to search engines to self-driving cars, AI (Artificial Intelligence) is used everywhere. It is simply the intelligence demonstrated by machines. that came into existence upon studying and analyzing the human brain.

What is this workshop about?

The CrushIT 3-Day workshop will take the student from various realms of artificial intelligence to the Linear and Logistic regression to decision tree and clustering. At the end of this workshop, the student will be able to:

  • Understand the growth of AI in different verticals
  • Understand how AI is automating the work humans did in the past 
  • Understand the role of AI in autonomous vehicles
  • Which mathematical models play an important role in building the AI software

With the scope of becoming one of the top five investment priorities by 2020, the future of AI holds a lot of opportunities. In the future, we might have AI that will analyze our speech and actions and interpret our needs as humans, AI that is more empathetic. AI is the technology of the future and this workshop will act as a kick-starter for people who are interested in it and who would like to make a career in AI.


  • Anyone who is interested in Artificial Intelligence can attend this workshop, although familiarity with Python programming will be helpful.
  • Interest in maths and learning new technologies
  • Each participant should bring their laptop

Why CrushIT?

The 3-Day CrushIT Artificial Intelligence workshop will start from the very basics of artificial intelligence and go on to advanced levels of discussing complex algorithms emphasizing on practical learning.

  • Interactive discussions and hands-on exercises
  • Highly skilled and dedicated instructors
  • Learn from Industry experts
  • Hybrid Training


  • Certificate of Participation
  • Certificate of Merit (for top performers) or Letter of Recommendation
  • A Chance of interning with our partner companies


  • Lessons 118
  • Quizzes 1
  • Duration 10 week
  • Language English
  • Students 0
  • Assessments Self
  • Categories ,

Course Curriculum

  • Introduction to Python Programming  0/10

    • What is Python?
    • Understanding IDLE
    • Python basics
    • String manipulation
    • Lists, tuples, dictionaries, variables
    • Control Structure – If loop, For loop and while Loop
    • Single line loops
    • Writing user-defined functions
    • Classes and OOPS concept
    • File Handling
  • Data Structure & Data Manipulation in Python  0/10

    • Introduction to Numpy Arrays
    • Creating Arrays, Matrices and Vectors
    • Indexing, Data Processing using Arrays
    • Mathematical computing basics
    • Basic statistics
    • File Input and Output
    • Introduction to Pandas
    • Data Acquisition
    • Selection and Filtering
    • Combining and Merging Data Frames
  • Understanding the Machine Learning Libraries  0/3

    • Numpy
    • Pandas
    • Opencv
  • Data visualization in Python   0/4

    • Introduction to Visualization
    • Working with Python visualization libraries
    • Matplotlib
    • Creating Line Plots, Bar Charts, Pie Charts, Histograms, Scatter Plots
  • Machine Learning: Introduction  0/7

    • Difference between Machine Learning, Data Science and AI
    • Regression vs. Classification
    • Features, Labels, Class
    • Supervised Learning and Unsupervised Learning Using Opencv
    • AI Algorithms
    • Cost Function
    • Optimizers
  • Linear Regression Using Opencv  0/8

    • Regression Problem Analysis
    • Mathematical modelling of Regression Model
    • Gradient Descent Algorithm
    • Use cases
    • Regression Table
    • Model Specification
    • L1 & L2 Regularization
    • Data sources for Linear regression
  • Math of Linear Regression   0/4

    • Linear Regression Math
    • Cost Function
    • Cost Optimizer: Gradient Descent Algorithm
    • Regression R Squared
  • Linear Regression – Case Study & Project  0/17

    • Programming Using Python
    • Building simple Univariate Linear Regression Model
    • Multivariate Regression Model
    • Apply Data Transformations
    • Identify Multicollinearity in Data Treatment on Data
    • Identify Heteroscedasticity
    • Modelling of Data
    • Variable Significance Identification
    • Model Significance Test
    • Bifurcate Data into Training / Testing Dataset
    • Build Model of Training Data Set
    • Predict using Testing Data Set
    • Validate the Model Performance
    • Best Fit Line and Linear Regression
    • Model Predictions
    • Model Accuracy
    • Graphical Plotting
  • Logistic Regression Using Opencv   0/18

    • Reason for the Logit Transform
    • Logit Transformation
    • Hypothesis
    • Variable and Model Significance
    • Maximum Likelihood Concept
    • Log Odds and Interpretation
    • Null Vs Residual Deviance
    • Chi-Square Test
    • ROC Curve
    • Model Specification
    • Case for Prediction Probe
    • Model Parameter Significance Evaluation
    • Drawing the ROC Curve
    • Optimization of threshold value
    • Estimating the Classification Model Hit Ratio
    • Isolating the Classifier for Optimum Results
    • Model Accuracy
    • Model Prediction
  • Support Vector Machine Using Opencv  0/11

    • Concept and Working Principle
    • Mathematical Modelling
    • Optimization Function Formation
    • The Kernel Method and Nonlinear Hyperplanes
    • Optimal separating hyperplane
    • Drawing Margins
    • Use Cases & Programming SVM using Python
    • Anomaly Detection with SVM
    • Use Cases & Programming using Python
    • Case study of KNN Vs SVM
    • Applying KNN & SVM for Supervised Problems of Classification & Unsupervised problems like Clustering
  • RANDOM FOREST & Decision Tree Algorithm  0/8

    • Concept and Working Principle
    • Mathematical Modelling
    • Optimization Function Formation
    • Analysis of Classification Problem case
    • Role of Gini Index in Decision Trees
    • Analysis of Regression Problem case
    • Use Cases & Programming using Python
    • Classification with Random Forest
  • Introduction to TensorFlow & Keras   0/11

    • Introduction Tensorflow
    • Tensorflow
    • MNIST
    • The Programming Model
    • Data Model, Tensor Board
    • Introducing Feed Forward Neural Nets
    • Softmax Classifier &ReLU Classifier
    • Deep Learning Applications
    • Working with Keras
    • Building Neural Network with keras
    • Examples and use cases
  • Unsupervised Learning Clustering:  0/7

    • Clustering Introduction
    • K-Means Clustering
    • Handling K-Means Clustering
    • Maths behind KMeans Clustering
    • K Means from scratch
    • Mean shift Introduction
    • Dynamically weight

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