Artificial Intelligence & Machine Learning

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This course provides basic to advanced knowledge on applying Artificial Intelligence in Machine Learning. This course does not require any prerequisite experience or awareness to enroll. Novice to Practitioners who looks to upskill can enroll as the course delivers foundational knowledge to practical know how on various types of machine learning algorithms with cases, demos and sample projects. It also includes required knowledge on Mathematics & Statistics, Python Programming required to excel in Machine Learning.

Course Objectives

At the completion of this course, learners will be able to

  • Comprehend the application of Artificial Intelligence in Machine Learning & Data science.
  • Gain application knowledge on statistical and mathematical techniques in machine learning.
  • Distinguish Deep Learning and Machine Learning models and algorithms.
  • Understand and apply supervised and unsupervised algorithm of machine learning to discover data patterns.
  • Understand datasets and structures using data visualization tools.
  • Apply various regression models to forecast and predict relationships between variables.
  • Understand the basic structure and components of decision tree and kNN and use them to use them optimally to analyze business case scenarios.
  • Comprehend SVM ( Support Vector Machines ) concepts and employ ensemble approaches such as Bagging and Boosting to produce better predictive performance.
  • Demonstrate Artificial Neural Networks (ANN) architectures to  model complex nonlinear relationships.

I Introduction to Artificial Intelligence

  • What Is Artificial Intelligence
  • Artificial intelligence Algorithms
  • Application of Artificial Intelligence
  • Level of product
  • Introduction Of Machine Learning
  • Application Of Machine Learning
  • Machine Learning Cloud Platforms
  • Objective Of Machine Learning Algorithm
  • Introduction of Deep Learning
  • Machine Learning VS Deep Learning
  • Application of Deep Learning.
  • Important Scientists to follow in the field of Machine Learning
  • Introduction to Data Science
  • Important Scientists to follow in the field of Machine Learning

III Data Analysis - Numpy & Pandas

Working with Numpy

  • NumPy Overview 
  • Properties, Purpose, and Types of Ndarray 
  • Class and Attributes of Ndarray Object 
  • Basic Operations: Concept and Examples 
  • Accessing Array 
  • Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays 
  • Shape Manipulation & Broadcasting 
  • Linear Algebra using numpy Stacking and resizing the array 
  • Random numbers using numpy
  • Working with Pandas
    • Data Structures 
    • Series, Data Frame & Panel 
    • Data Frame basic properties 
    • Importing excel sheets, csv files, executing sql queries 
    • Importing and exporting json files 
    • Data Selection and Filtering 
    • Selection of columns and rows 
    • Filtering Data frames 
    • Filtering – AND operation and OR operation

IV Data Visualization - Matplotlib & Seaborn

  • Categorical Plot
  • Continuous Plot
  • Distribution Plot
  • Statistical Plot

V Linear regression

  • The conceptual idea of linear regression
  • Predictive Equation
  • Cost function formation
  • Gradient Descent Algorithm
  • OLS approach for Linear Regression
  • Multivariate Regression Model
  • Correlation Analysis – Analyzing the dependence of variables
  • Apply Data Transformations
  • Overfitting
  • L1 & L2 Regularization
  • R2, RMSE
  • Project: Predictive Analysis using Linear Regression

VI Logistic Regression

  • Classification Problem Analysis
  • Variable and Model Significance
  • Sigmoid Function
  • Cost Function Formation
  • Mathematical Modelling
  • Model Parameter Significance Evaluation
  • Implementing logistic regression using Scikit learn
  • Performance analysis for classification problem
  • Confusion Matrix Analysis
  • Accuracy, recall, precision and F1 Score
  • Specificity and Sensitivity
  • Classification Report Analysis
  • Estimating the Classification Model
  • Project: Predictive Analysis using Logistic Regression

VII KNN ( K Nearest Neighbour) & Decision Tree

Understanding the KNN 

    • Distance metrics 
    • KNN for Regression & classification 
    • Implementing KNN using Python 
    • Case Study on KNN 
    • Handling overfitting and underfitting with KNN
  • Forming Decision Tree 
    • Components of Decision Tree 
    • Mathematics of Decision Tree 
    • Entropy Approach 
    • Gini Entropy Approach 
    • Variance – Decision Tree for Regression 
    • Decision Tree Evaluation 
    • Overfitting of Decision Tree 
    • Visualizing Decision Tree using graphviz

VIII SVM & Ensemble Learning

  • Support Vector Machines 
    • Concept and Working Principle 
    • Mathematical Modelling 
    • Optimization Function Formation 
    • Slack Variable 
    • The Kernel Method and Nonlinear Hyperplanes 
    • Use Cases 
  • Ensemble Learning

    • Concept of Ensemble Learning 
    • Bagging and Boosting 
    • Bagging – Random Forest 
    • Random Forest for Classification 
    • Random Forest for Regression 
    • Boosting – Gradient Boosting Trees 
    • Boosting – Adaboost
    • Programming SVM using Python 
    • Project – Character recognition using SVM

IX Unsupervised Learning

  • Clustering
    • Application of Clustering 
    • Hierarchical Clustering 
    • K Means Clustering 
    • Use Cases for K Means Clustering 
  • Dimensionality Reduction – PCA
    • Dimensionality Reduction, Data Compression 
    • Curse of dimensionality 
    • Multicollinearity 
    • Factor Analysis 
    • Concept and Mathematical modelling 
    • Use Cases 
    • Programming using Python

X Artificial Neural Networks

  • Introduction to Neural Networks 
  • Working of Neural Networks 
  • Mathematical modelling of Neural Networks 
  • Architectures of ANN 
  • ANN learning process

XI Capstone Project

  • Working Final Project 
  • Splitting final Project into phases 
  • Working on structuring project
Kharthik Nandagopal photo

Kharthik Nandagopal

PCM ACP Professional
I would like to thank Quadra plus for their excellent support throughout and a special thanks to our coach Ms. Durga for her wonderful support and dedication. Her knowledge and command over the subject is inspiring and at the same time she ensured that the classes were enjoyable and lively. I would definitely recommend Quadra plus to my colleagues and I shall surely miss these interactions with the batch and also with Durga. I sincerely wish Quadra plus the very best and once again a big thanks to Durga for everything.

50+ hours of live interactive learning

Capstone and 10+ real life AI projects

  High-Quality Lab Environment

  Work on 10+ important AI libraries

  Standard Books and PPTs

  Experience on Kaggle competitions

  Exam Preparation and job interviews support

   KHDA Approved Certificate

  Free Retraining

  Free entry pass to webinars

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