Course Content

Syllabus:

Full Stack Data Science Program

in Artificial Intelligence, Machine Learning and Deep Learning

Program Details

Python

• Python Installation

• Jupyter Notebook Tutorial

• Variable

• Function

• Lambda Expression

• Loops

• List

• Tuple

• Set

• Dictionary

• Coding Test-1

• Assignment-1

• Assignment-2

• Assignment-3

Advance Python

• Introduction to Numpy

• Creating Arrays

• Selection and Indexing

• Basic Operations on Arrays

• Mathematical Operation on Arrays

• Linear Algebra Operation on Arrays

• Stacking Arrays

• Data Types and Type Conversion

• Assignment-4

• Introduction to Pandas

• Creating Data Frames

• Reading and Writing Operation

• Selection and Indexing

• Conditional Selection

• Assignmet-5

• Groupby

• Pivot Table

• Merge

• Join

• Concat

• Assignment-6

• Missing Value Treatment

• Drop Duplicates

• Dealing with Date Time Data

• Apply()

• Introduction to Series

• Series Operation

• Pandas Builtin Functions for Data Visualisation

• Assignment-7

• Coding Test-2

Visualisation

• Introduction To Plotly

• Scatter Plot

• Line Plot

• Scatter Matrix

• Box Plot

• Bar Chart

• Histogram

• Sun Burst Chart

• Create DashBoard

Statistics

• Central Limit Theorem

• Measure of Dispersion

• Quartiles

• Inter Quartile Ranges

• Variance

• Standard Deviation

• Z Score

• Normal Distribution

• Pearson Correlation Coefficient- R

• R Square

• Adjust R2

• Multi Colinearity Detection Techniques

• Multi Colinearity Removal Techniques

• Outliers Detection and Removal

• Assignment-8

Machine Learning

• Introduction to Machine Learning

• Difference Between Supervised & Unsupervised Learning

• Difference Between Classification and Regression

• Machine Learning Application

• Data Science Project Life Cycle

• Linear Regression

• Theory of Linear Regression

• Cost Function

• Optimization Using Gradient Descent

• Mathematical Interpretation of Gradient Descent

• Project-1 – Sales Prediction Project

• Understanding Why Linear Regression may fail?

• Model Validation Techniques

• Mean Squared Error

• Root Mean Squared Error

• Mean Absolute Error

• Polynomial Regression

• Understanding Polynomial Regression

• Implementing Polynomial Regression Using Python

• Overfitting, Underfitting, Right Fit

• Coding Test- 2- Project-2 (Finance project)

• Logistic Regression

• Understanding Logistic Regression Step by Step

• Project-3 – Retail Project

• Decision Tree and Random Forest

• ID3 Algorithm vs CART

• Entropy

• Information Gain

• Step by Step Understanding of How Decision Tree Work

• How to overcome overfitting in DT

• Cross Validation

• Bootstrap Aggregation/Bagging

• Introduction to Random Forest

• How Random Forest Works

• Feature Selection

• Model Validation Techniques

• Accuracy

• Confusion Matrix

• Classification Report

• Recall

• Precision

• Project-4- Healthcare Project

• Coding Test-5 – Project-5(Banking Project)

• Hyper parameter Tuning

• KMeans Clustering

• What is Euclidian Distance

• Introduction to Unsupervised Learning

• Step By Step Mathematical Derivation

• Pros and Cons Of K Means

• Elbow Method to Find K

• Project-6- Customer Segmentation

Deep Learning

• What is Deep Learning

• Deep Learning VS Machine Learning

• What is a Perceptron

• How Neural Network Learns

• Multi Layer Perceptron

• Activation Function

• Introduction to Keras

• What is Feed Forward Network

• Detail Explanation of ANN

• What is Cost Function

• Optimization Technique

• Vanilla Gradient Descent

• Mini Batch Gradient Descent

• Stochastic Gradient Descent

• Softmax

• Cross Entropy Loss

• MSE vs Cross Entropy

• Project-7 - Price Prediction Project

• Projet-8- Coding Test- Classification Project(IOT Data- Aviation Domain)

Image Processing , CNN & Computer Vision

• Introduction to Computer Vision

• Challenges in Computer Vision

• Introduction to Open CV

• Image Basics

• Reading and Writing Images/Videos

• Rescaling / Normalisation

• Color Mapping

• Thresholding of an Image

• Morphological Transformation

• Image Augmentation Using Keras

• What is Image Filters

• Different Kind of Filters

• Convolution

• What is Convolutional Neural network

• Pooling

• Overfitting In Deep Learning

• Drop Outs

• Project-9- X-ray Image Classification(HealthCare)

Time Series Analysis

• What is Time Series Data

• Resampling

• Time Shifting

• Interpolation

• Missing Value Treatment in Time Series

• Trend

• Seasonality

• Auto Correlation

• Time Series Decomposition

• Moving Average

• Exponential Moving Average

• Time Series Modelling Using Facebook Prophet

• Project-10- Time Series Forecasting Project

Natural Language Processing-Text Mining

• What is Unstructured Data

• Introduction to NLTK and Spacy

• Tokenization

• Stop Word Removal

• Stemming

• Lemmatization

• N-Grams

• What is Word Embedding

• Count Vectorizer

• Tf-Idf Vectorizer

• Pattern Matching

• Regular Expression

• Project-11 – Sentiment Analysis(Social Media Data)

• Project-12- Document Clustering (News Data)

Big Data Analytics - Apache Spark

• Introduction to Apache Spark

• Parallel vs Distributed Computing

• Introduction to Big Data

• Spark Installation

• Spark Vs Hadoop

• Spark Architecture

• Lazy Evaluation

• RDD

• Spark SQL & DataFrame

• Spark ML Lib

• Project-13- Retail Domain Project using Spark MLLib