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B-TECH in Machine Learning at Shoolini University of Biotechnology and Management Sciences
Solan, Himachal Pradesh
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About the Specialization
What is Machine Learning at Shoolini University of Biotechnology and Management Sciences Solan?
This B.Tech Computer Science and Engineering with a specialization in Machine Learning program at Shoolini University focuses on equipping students with advanced skills in artificial intelligence, data analytics, and intelligent system design. The curriculum is meticulously crafted to meet the burgeoning demands of the Indian IT industry for skilled professionals in cutting-edge technologies like predictive modeling and automation, preparing graduates for a future-ready career.
Who Should Apply?
This program is ideal for 10+2 science graduates with a strong aptitude for mathematics and computing, seeking entry into the dynamic fields of AI and data science. It also caters to aspiring innovators and problem-solvers who wish to contribute to technological advancements and are interested in designing intelligent software solutions and systems relevant to various Indian industry sectors.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as Machine Learning Engineers, Data Scientists, AI Developers, or Research Analysts across e-commerce, healthcare, finance, and manufacturing sectors. With competitive entry-level salaries in the range of INR 5-8 LPA and significant growth trajectories for experienced professionals, this specialization aligns well with the country''''s digital transformation initiatives and global tech demand.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate early semesters to building a robust foundation in C++ and Python programming, alongside data structures and algorithms. Utilize online competitive programming platforms like CodeChef and HackerRank to practice daily problem-solving, which is crucial for technical interviews.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, NPTEL courses on DSA
Career Connection
Strong programming fundamentals are non-negotiable for all tech roles and form the bedrock for advanced machine learning concepts, significantly boosting chances in placement drives.
Engage in Early Project-Based Learning- (Semester 1-2)
Start building small, practical projects using basic programming skills. For instance, create a simple calculator, a text-based game, or a basic data analysis script. Document your code and use GitHub to version control and showcase your work. Participate in university coding clubs.
Tools & Resources
GitHub, VS Code, Python Libraries (NumPy, Pandas for basic tasks)
Career Connection
Early projects demonstrate initiative and practical application of theoretical knowledge, making your profile stand out during internship and entry-level job applications.
Cultivate Strong Mathematical Foundations- (Semester 1-2)
Regularly revisit and deepen understanding of key mathematical concepts like linear algebra, calculus, and probability. These are foundational for understanding machine learning algorithms at a conceptual level. Supplement classroom learning with online resources and practice problems.
Tools & Resources
Khan Academy, NPTEL Mathematics courses, 3Blue1Brown YouTube Channel
Career Connection
A solid mathematical background is essential for comprehending advanced ML algorithms, developing new models, and excelling in research-oriented or specialized data science roles.
Intermediate Stage
Deep Dive into Core ML Concepts- (Semester 3-5)
Beyond classroom lectures, delve deeper into machine learning algorithms such as linear regression, logistic regression, decision trees, support vector machines, and basic neural networks. Implement these algorithms from scratch using Python libraries like Scikit-learn, and understand their underlying math.
Tools & Resources
Scikit-learn, TensorFlow/Keras (basics), Coursera/edX ML courses, Andrew Ng''''s Machine Learning course
Career Connection
Mastering these core concepts enables you to tackle real-world ML problems and confidently answer technical questions in interviews for ML engineering or data scientist roles.
Participate in Data Science Competitions- (Semester 3-5)
Actively join data science competitions on platforms like Kaggle. This provides hands-on experience with real-world datasets, exposure to diverse problem-solving techniques, and an opportunity to learn from top practitioners. Collaborate with peers to maximize learning.
Tools & Resources
Kaggle, Analytics Vidhya, DataCamp
Career Connection
Winning or even actively participating in Kaggle competitions builds a strong portfolio, showcases your practical skills, and provides excellent talking points for job interviews.
Seek Industry Internships- (Semester 3-5)
Actively apply for internships in ML/AI roles during summer and winter breaks. Leverage the university''''s career services, LinkedIn, and other job portals. Focus on gaining practical exposure to industry workflows, tools, and challenges.
Tools & Resources
LinkedIn Jobs, Internshala, University Placement Cell
Career Connection
Internships are crucial for bridging the gap between academia and industry. They often lead to pre-placement offers and provide invaluable experience that makes you job-ready.
Advanced Stage
Specialize and Build a Robust Portfolio- (Semester 6-8)
Choose advanced areas like Deep Learning, Natural Language Processing, or Computer Vision and pursue specialized projects. Develop end-to-end ML applications, deploy models, and create a comprehensive GitHub repository, personal website, or technical blog to showcase your expertise.
Tools & Resources
PyTorch, AWS/GCP/Azure ML services, Streamlit/Flask for deployment, Medium/Hashnode for blogging
Career Connection
A strong, specialized portfolio with deployed projects is your most powerful tool for attracting top employers and securing high-paying roles in niche ML domains.
Prepare for Technical Interviews and Aptitude Tests- (Semester 6-8)
Intensively practice coding questions on platforms like LeetCode and GeeksforGeeks, focusing on data structures, algorithms, and system design. Simultaneously, work on communication skills to articulate your thought process clearly and concisely during interviews.
Tools & Resources
LeetCode, GeeksforGeeks, Pramp (mock interviews), Cracking the Coding Interview book
Career Connection
Thorough preparation for technical and HR interviews is directly correlated with securing placements in leading tech companies and startups.
Network with Industry Professionals and Alumni- (Semester 6-8)
Attend industry conferences, workshops, and webinars focused on AI and ML. Actively engage with professionals on LinkedIn, participate in alumni meets, and seek mentorship. Build a professional network to gain insights, identify opportunities, and receive guidance.
Tools & Resources
LinkedIn, Industry conferences (e.g., Cypher, GIDS), University alumni network events
Career Connection
Networking can open doors to hidden job opportunities, valuable mentorship, and insights into career progression, crucial for navigating the competitive Indian tech landscape.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject/Computer Science/Information Technology/Informatics Practices/Agriculture/Engineering Graphics/Business Studies from a recognized Board with a minimum of 50% aggregate marks (45% for SC/ST/OBC).
Duration: 8 semesters / 4 years
Credits: Credits not specified
Assessment: Internal: 40% (for theory subjects), External: 60% (for theory subjects)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA101 | Engineering Mathematics – I | Core | 4 | Differential Calculus, Integral Calculus, Sequences and Series, Multivariable Calculus |
| PH101 | Engineering Physics – I | Core | 3 | Wave Optics, Quantum Mechanics, Solid State Physics, Lasers and Fiber Optics |
| PH102 | Engineering Physics Lab – I | Lab | 1 | Experiments on Interference, Diffraction, Polarization, Semiconductor Devices |
| CS101 | Programming For Problem Solving | Core | 3 | Introduction to Programming, Control Structures, Functions, Arrays and Pointers, Structures and Unions |
| CS102 | Programming For Problem Solving Lab | Lab | 1 | C Programming Exercises, Debugging Techniques, Implementation of Algorithms |
| HS101 | English for Professionals | Core | 2 | Grammar and Vocabulary, Reading Comprehension, Business Communication, Presentation Skills |
| HS102 | English for Professionals Lab | Lab | 1 | Public Speaking Practice, Group Discussions, Interview Skills, Professional Writing |
| EE101 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Basic Electronic Devices |
| EE102 | Basic Electrical Engineering Lab | Lab | 1 | Ohm''''s Law Experiments, Network Theorems, PN Junction Diode Characteristics |
| ME101 | Engineering Graphics & Design | Core | 1 | Orthographic Projections, Isometric Views, Sectional Views, Introduction to CAD |
| ME102 | Engineering Graphics & Design Lab | Lab | 1 | 2D and 3D Drafting Practice, Geometric Dimensioning and Tolerancing |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA201 | Engineering Mathematics – II | Core | 4 | Matrices, Vector Calculus, Ordinary Differential Equations, Laplace Transforms |
| CH201 | Engineering Chemistry | Core | 3 | Water Technology, Fuel and Combustion, Polymers, Corrosion, Electrochemistry |
| CH202 | Engineering Chemistry Lab | Lab | 1 | Water Analysis, Fuel Testing, Polymer Synthesis |
| CS201 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Techniques |
| CS202 | Data Structures Lab | Lab | 1 | Implementation of Data Structures, Algorithm Analysis, Problem Solving with Data Structures |
| CS203 | Object Oriented Programming Using C++ | Core | 3 | Classes and Objects, Inheritance, Polymorphism, Exception Handling, Templates |
| CS204 | Object Oriented Programming Using C++ Lab | Lab | 1 | C++ Programming Exercises, OOP Concepts Implementation |
| HS201 | Environmental Sciences | Core | 2 | Ecosystems, Biodiversity, Environmental Pollution, Renewable Energy Sources, Environmental Management |
| CE201 | Basic Civil Engineering | Core | 2 | Building Materials, Surveying, Structural Elements, Water Resources, Transportation Engineering |
| ME201 | Basic Mechanical Engineering | Core | 3 | Thermodynamics, IC Engines, Refrigeration and Air Conditioning, Power Transmission, Manufacturing Processes |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS301 | Computer Organization & Architecture | Core | 3 | Digital Logic Circuits, CPU Organization, Memory Hierarchy, Input/Output Organization, Pipelining |
| CS302 | Design & Analysis of Algorithms | Core | 3 | Asymptotic Notations, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| CS303 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| CS304 | Database Management Systems | Core | 3 | ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| CS305 | Database Management Systems Lab | Lab | 1 | SQL Practice, Database Design, DBMS Project |
| CS306 | Operating Systems Lab | Lab | 1 | Linux Commands, Shell Scripting, Process and Thread Programming |
| CS307 | Python Programming | Core | 3 | Python Fundamentals, Data Structures in Python, Functions and Modules, File I/O, Object-Oriented Programming in Python |
| CS308 | Python Programming Lab | Lab | 1 | Practical Python Scripting, Data Manipulation with Pandas, Web Scraping |
| EC301 | Digital Electronics | Core | 3 | Logic Gates and Boolean Algebra, Combinational Circuits, Sequential Circuits, Counters and Registers |
| GE310 | Data Mining | Generic Elective | 3 | Data Preprocessing, Association Rule Mining, Classification Techniques, Clustering Algorithms, Outlier Detection |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS401 | Discrete Structures | Core | 3 | Set Theory, Relations and Functions, Propositional Logic, Graph Theory, Recurrence Relations |
| CS402 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Undecidability |
| CS403 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Software Testing, Project Management |
| CS404 | Computer Networks | Core | 3 | OSI Model, TCP/IP Protocol Suite, Network Layer Protocols, Transport Layer Protocols, Application Layer Protocols |
| CS405 | Computer Networks Lab | Lab | 1 | Network Configuration, Socket Programming, Packet Analysis |
| CS406 | Software Engineering Lab | Lab | 1 | CASE Tools, Software Documentation, Testing Tools |
| ML401 | Machine Learning | Core | 3 | Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation and Selection |
| ML402 | Machine Learning Lab | Lab | 1 | Python for Machine Learning, Implementing ML Algorithms, Data Preprocessing, Model Training and Testing |
| OE401 | Introduction to AI | Open Elective | 3 | History of AI, Problem Solving Agents, Search Algorithms, Knowledge Representation, Expert Systems |
