Post Graduation in Data Science with AI & ML

Data science plays a crucial role in today’s digital era, enabling organizations to make data-driven decisions and gain a competitive advantage. In this course, you will learn how data science impacts various industries, including finance, healthcare, and marketing. By understanding its importance, you will be equipped with the knowledge and skills to harness the power of data and drive innovation in your field.
In this dynamic and comprehensive course, we will dive deep into the world of data science, exploring its immense potential and practical applications. Through hands-on projects and expert guidance, you will acquire the necessary skills to extract insights from complex datasets and unlock the power of data-driven decision-making. Get ready to embark on an exciting journey of discovering the limitless possibilities of data science!

Objectives:- After Data Science with AI Course student will be able to:

• Scrap web site
• Data Cleaning and Visualization
• SQL developer
• Data Engineer
• Data Scientist Intern
• ML Engineer
• NLP Engineer
• JR Data Scientist
• CV Engineer
• RPA Developer

Brief Contents (Syllabus)

Logic Building

•  Introduction to programming languages
•  Introduction to Algorithms
• Baseline for programming Language

Structured Query Language(SQL)

Introduction to SQL

• What is Data and Database l What is Database Management System l What is SQL?
• SQL vs MYSQL

DDL & DML

• Data types, expressions, operators
• Data Definition Language (DDL)
• Creation of table
• Dropping a table
• INSERT statement
• UPDATE statement
• DELETE statement

DQL

• SELECT statement
• WHERE clause search condition
• Arithmetic, Comparison and Logical operator
• Range operator
• List operator
• Like operator
• Using ORDER BY, DISTINCT and TOP
• Using IS NULL and IS NOT NULL

Built in SQL Functions

• String Function
• Math Function
• Date Function
• Aggregate Function
• GROUP BY clause with HAVING
• HAVING VS WHERE

Joins and Sub query

• Introduction to Joins
• Types of Joins
• What are nested/sub queries
• Introduction Types of Sub-queries

Stored procedure and triggers and window functions

• Understanding meaning of stored procedure
• Creating first stored procedure using SQL
• What are triggers?
• Creating triggers for beginners
• Introduction to window functions

Python Programming

• Python Installation and Basics
• Syntax and programming Structures
• Variables, Operators, Keywords, Expressions
• Decision Making: if, elif, else
• Loops: while, for, break, continue, pass
• List, Tuple, Dictionary, Set
• Functions, Modules
• Object Oriented Programming
• Exception handling
• File handling
• Web scrapping and regular expression (RegEx)

CASE STUDY -: IMDB TOP 250 MOVIE DATA WEB SCRAPPIN

Introduction statistics and Data science

• What is data science
• Data science vs data analysis
• Understanding data
• Descriptive statistics
• Inferential statistics

Libraries for data manipulation and data visualization

• Introduction to numpy and its functions
• Introduction to pandas and its functions
• Introduction to matplotlib and seaborn for data visualization

CASE STUDY -: IPL DATA ANALYSIS , NETFLIX AND HOTSTAR DATA ANALYSIS

Machine Learning

Introduction to Machine Learning

• What is Machine Learning
• Applications of Machine Learning
• Supervised Vs Unsupervised Machine Learning
• Regression vs classification

Exploratory Data Analysis (EDA)

• Finding null values
• Detecting and removal of outliers
• Feature scaling – : Standardization and normalization

Introduction to Linear Regression

• What is regression?
• What is linear regression
• Building First ML model for marks prediction
• Simple linear regression
• Multiple Regression
• Polynomial Regression
• Error functions in Regression (MAE . MSE , RMSE )
• Calculating accuracy using R2Score

CASE STUDY -: Car Price Prediction on cars24 dataset.

Introduction to Overfitting and underfitting

• Overfitting Vs underfitting
• Bias-Variance Tradeoff
• Regularization Techniques -: Ridge and Lasso
• Understanding and demonstrating Ridge and lasso regression techniques.
• Cross Validation Techniques

Introduction to Logistic Regression

• Sigmoid function
• Understanding parameters of logistic regression
• ROC AUC Curve
• Confusion Matrix -: Precision , Recall , accuracy , f1 Score

Introduction to KNN

• Understanding working of K – Nearest Neighbors
• Advantages and drawbacks of using KNN
• KNN for regression

Introduction to SVM

• Understanding Support Vector Machine
• Hard and soft margin
• Understanding Support Vectors , Hyperplane
• Kernel technique
• SVM for regression

Introduction to SVM

• Understanding Naive Bayes Theorem
• Introduction to text classification
• NLP pipeline
• Vectorization of text data
• Case Study -: Spam mail classification using naive bayes

Naive Bayes Classifier

• Understanding Support Vector Machine
• Hard and soft margin
• Understanding Support Vectors , Hyperplane
• Kernel technique
• SVM for regression

Decision Tree classifier

• Working of DT
• Gini Index and Entropy
• Pruning techniques
• Advantages and disadvantages of Decision Tree
• Decision Tree for regression

Introduction to Ensemble learning

• What is Bagging?
• Random Forest Classifier
• ADA Boost , XGboost, Gradient Boost

Unsupervised Machine Learning Algorithm

• Clustering
• K-means Clustering
• Hierarchical clustering
• Association rules
• PCA (principle component analysis)

Project deployment using Flask Framework.

• Clustering
• K-means Clustering
• Hierarchical clustering
• Association rules
• PCA (principle component analysis)

CASE STUDY ON BREAST CANCER DETECTION USING CLASSIFICATION ALGORITHMS

CASE STUDY ON FRAUD DETECTION USING CLASSIFICATION ALGORITHMS

Introduction to Deep Learning

• Artificial Neural Network (ANN)
• What is Deep Learning
• DL vs ML
• Forward and backward propagation
• Activation functions
• Optimizer
• Early stopping and dropout layer to handle overfitting

CASE STUDY – DIGIT CLASSIFICATION USING ANN

Introduction to Computer Vision

• Image pre-processing
• Detecting edges
• Understanding Convolutional layer and pooling layer
• Image classification using CNN
• Image Augmentation
• Reading text data from an image.
• Case Study -: Hand gesture volume controller using MediaPipe
• Case Study -: AI Exercise counter using MediaPipe

Certificate

• MIT Certification

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Note: We provide Industrial based training as we provide Engineering Design Services  under Milestone PLM Solutions PVT LTD.