Data Science and AI

This is a "MASTER PROGRAM IN DATA SCIENCE" designed for all. It includes world-class instruction, outcome-centric boot camps & hands-on projects. This Data science course will give you insights into real-world data science applications.

Next Courses Start : 10th Nov 2023

Program Duration 6 Months

Course Overview

Data Science and AI Course Overview

This is a "MASTER PROGRAM IN DATA SCIENCE" designed for all. It includes world-class instruction, outcome-centric boot camps & hands-on projects. This Data science course will give you insights into real-world data science applications.

This Courses Includes

Data Science and AI Course Includes

    • Hands on Experience
    • Real Time project work
    • Skill based Training
    • Certificate of Completion

Course Contents

Data Science and AI Course contents

Topic:

  • Introduction to Python
  • Getting started with Python
  • Hands-on Data Types
  • Operators
  • Flow Control
  • Functions
  • Types of Parameter / Arguments
  • Local Variables and Global Variables
  • Function as a parameter of another function
  • Lambda Function
  • Decorator
  • Constructors
  • Inheritance
  • Types of Inheritance
  • Python Miscellaneous
  • Regular Expressions
  • Python Libraries
  • Numpy
  • Pandas

Topic:

  • Introduction to Numpy
  • Creating numpy array
  • Attributes of numpy array
  • Advantage of Numpy array over List
  • Mathematical operation on numpy array
  • Different ways to create numpy array
  • Reshaping numpy array
  • Concatenation and splitting operation
  • Random sample generation

Topic:

  • Pandas series
  • Pandas data frame
  • Reading CSV files
  • Read excel files
  • Handling missing values
  • categorical data
  • Data cleaning and pre processing

Topic:

  • What is machine learning?
  • What is Deep learning?
  • What is data science
  • Difference between DS , ML, DL
  • Supervised learning
  • Unsupervised learning
  • Regression
  • Linear Regression
  • Multiple linear regression
  • Polynomial Regression
  • Logistic regression
  • K-Nearest Neighbors Algorithm
  • How KNN works
  • KNN classifier
  • Confusion Matrix
  • KNN Regressor
  • How to choose k value
  • Naïve Bayes Algorithm
  • Bayes Theorem
  • Types of naïve bayes classifier
  • Bernoulli naïve bayes
  • Gaussian Naïve
  • Multinomial NB
  • Decision Tree Algorithm
  • Why to use decision trees?
  • Decision Tree Terminologies
  • How a decision tree works
  • Advantages and disadvantages
  • Random Forest Algorithm
  • What is random forest
  • How random forest works
  • Ensemble learning
  • Bagging and boosting
  • Advantages and disadvantages
  • Support vector machine
  • What is support vector machine?
  • Types of SVM
  • Hyper plane and support vectors
  • How support vector works?
  • Unsupervised Learning
  • What is unsupervised learning
  • Types of unsupervised learning
  • Applications of unsupervised learning
  • K-means clustering
  • Feature engineering and Dimensional Reduction
  • feature extraction
  • feature selection
  • dummy variable and one hot encoding
  • Label encoding and ordinal encoding
  • Feature scaling
  • Hyper parameter tuning
  • Model selection
  • What is Model Selection?
  • The need for Model Selection
  • Cross-Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms

Topic:

  • Feature engineering and Dimensional Reduction
  • feature extraction
  • feature selection
  • dummy variable and one hot encoding
  • Label encoding and ordinal encoding
  • Feature scaling
  • Hyper parameter tuning
  • Model selection
  • What is Model Selection?
  • The need for Model Selection
  • Cross-Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Time series prediction
  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Neural Network
  • What is Neural Network
  • What is neuron and how it works
  • How neural network works
  • Perceptron and multi-layer neural network
  • Types of neural networks
  • Implementing ANN using keras
  • CNN

Topic:

  • Applications of Text Mining
  • Reading, Writing to text and word files
  • Setting the NLTK Environment
  • Accessing the NLTK Corpora
  • Tokenization
  • Frequency Distribution
  • Different Types of Tokenizers
  • Stemming
  • Lemmatization
  • Stopwords

Topic:

  • Convolution Neural Network
  • Image Classification Example
  • What is Convolution
  • Convolutional Layer Network
  • Convolutional Layer
  • Filtering
  • ReLU Layer
  • Pooling
  • Data Flattening
  • Fully Connected Layer
  • Predicting a cat or a dog
  • Saving and Loading a Model
  • Face Detection using OpenCV
  • Regional CNN
  • Regional-CNN
  • Selective Search Algorithm
  • Bounding Box Regression
  • SVM in RCNN
  • Pre-trained Model
  • Model Accuracy
  • Model Inference Time
  • Model Size Comparison
  • Transfer Learning
  • Object Detection – Evaluation
  • mAP
  • IoU
  • RCNN – Speed Bottleneck
  • Fast R-CNN
  • RoI Pooling
  • Fast R-CNN – Speed Bottleneck
  • Faster R-CNN
  • Feature Pyramid Network (FPN)
  • Regional Proposal Network (RPN)
  • Mask R-CNN
  • Boltzmann Machine & Autoencoder
  • What is Boltzmann Machine (BM)?
  • Identify the issues with BM
  • Why did RBM come into the picture?
  • Step-by-step implementation of RBM
  • Distribution of Boltzmann Machine
  • Understanding Autoencoders
  • Architecture of Autoencoders
  • Brief on types of Autoencoders
  • Applications of Autoencoders
  • Generative Adversarial Network(GAN)
  • Which Face is Fake?
  • Understanding GAN
  • What is Generative Adversarial Network?
  • How does GAN work?
  • Step by step Generative Adversarial Network implementation
  • Types of GAN
  • Recent Advances: GAN

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