Post Graduation in Data Science with Certification and Placement

Transform Your Career with Post Graduation in Data Science with AI & ML

Step into the future with our Post Graduation in Data Science course with AI & ML. Covering data science fundamentals, machine learning, and artificial intelligence, this program prepares you for advanced data science and AI roles with practical, hands-on experience.

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All Levels
24 Weeks
MIT Certification
Industry Immersion
Capstone Projects

Overview

This Post Graduation in Data Science course combines data science with artificial intelligence and machine learning. Learn to manage and analyze data, apply machine learning algorithms, and explore AI techniques. With hands-on projects and case studies, you'll gain the expertise needed for high-demand AI and data science roles.

Data Scientist
Machine Learning Engineer
AI Engineer
Data Engineer
NLP Engineer
RPA Developer

Targeted Job
Roles

Training and Methodology

By enrolling in this course, you will gain access to -

Integrated Learning: - Combining data science with AI and ML.
Hands-On Projects - Real-world case studies and practical exercises.
Expert Instruction - Guidance from industry professionals.

Why Choose This
Course?

Lead the Future with Expertise in Data Science and AI. Our program provides a deep understanding of data science and advanced AI techniques, preparing you for leadership roles. Gain practical skills and industry experience to drive data science and AI innovation.

100% Placement Assistance Program

Job placement assistance readiness.
Real time projects

Apply skills through industry-relevant projects.
Reviews and Feedback

Stay on track with regular reviews and feedback.

Skills acquired from this course

Advanced data manipulation and analysis techniques
Expertise in machine learning algorithms and models
Proficiency in AI technologies and applications
Hands-on experience with real-world projects and case studies

Tools & Languages Included In This course

The Course Syllabus

The course covers important topics related to Data Science.

Overview of Data Science
- Data Science Fundamentals
- Data Manipulation and Analysis
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)g
- CASE STUDY -: IMDB TOP 250 MOVIE DATA WEB SCRAPPIN
- 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
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
- Naive Bayes Classifier
  - Understanding Naive Bayes Theorem
  - Introduction to text classification
  - NLP pipeline
  - Vectorization of text data
  - Case Study -: Spam mail classification using naive bayes
  - 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
- 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
Artificial Intelligence
- AI Concepts and Techniques
- Neural Networks and Deep Learning
- AI Applications and Tools
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
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
Course Project
- Comprehensive Data Science and AI Project

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Certification For This
Course

Receive a recognized certification upon course completion, validating your skills and boosting your career prospects.

MIT Certification - Post Graduation in Data Science

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Frequently Asked Questions

Find answers to all your questions about our diverse course categories. Discover the range of subjects we offer, and learn how to choose the right courses to match your interests and career goals. Let us guide you in navigating our extensive catalog to find the perfect fit for your educational journey.

Who can enroll for this course?

Any person with ME, MTech, BE, BTech, BCA, MCA, Msc, Bsc in Computer Science, Bsc IT, Msc IT, Diploma, IT, AI, ML can register for this course.
What will be the mode of delivery?

We offer 3 delivery models
1) Classroom
2) Live Online
3) Recorded lectures
Kindly contact us with your requirements.
Will I receive a certificate after completion of this course?

Yes, you will be getting a certificate from Milestone Institute of Technology.
Does this course align with Industry requirements?

Yes, at MIT we ensure our syllabus and exercises are up to date as per industry requirements. We have used industry examples wherever possible in the course material. Additionally, you can also register your interest in Industry internships opportunities with our placement department.

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