Artificial Intelligence Course

|| Certified Artificial Intelligence Training in Dahod

The Artificial Intelligence Course at BIT Dahod is an intensive, future-ready program designed to equip students with the knowledge and practical skills needed to thrive in the rapidly evolving world of intelligent systems. This course blends foundational AI theory with hands-on experience, starting with essential concepts such as intelligent agents, logic-based systems, and classical search algorithms, then advancing into machine learning, deep learning, and natural language processing. Students will gain experience working with tools and frameworks like Python, OpenCV, TensorFlow, and PyTorch, enabling them to create AI models that can perceive, learn, and make decisions. The course takes a problem-solving approach, guiding learners through real-world case studies involving robotics, smart healthcare, speech recognition, fraud detection, and AI in IoT ecosystems. A key highlight of the program is its focus on ethical AI and responsible innovation, ensuring students understand the societal impact of the technology they develop. Topics like reinforcement learning, computer vision, and AI for automation are explored in depth, supported by practical labs and collaborative projects. Students will not only build and train intelligent systems but also learn how to optimize and deploy them in real-world settings. By the end of the course, participants will have developed a strong portfolio showcasing their ability to implement cutting-edge AI solutions, preparing them for high-demand roles such as AI developer, machine learning engineer, data scientist, and AI research associate. With its balanced blend of theory, tools, and real-world applications, the Artificial Intelligence course at BIT Dahod empowers students to become innovators and leaders in the AI-driven digital era.

|| Choose Artificial Intelligence Course From BIT

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|| What will I learn?

Understand the basic principles and history of artificial intelligence.
Apply search algorithms for problem-solving.
Represent knowledge using logical and probabilistic models.

Develop and evaluate machine learning models.
Implement natural language processing techniques.
Understand the principles of robotics and autonomous systems.

|| What will I learn?

Understand the basic principles and history of artificial intelligence.
Apply search algorithms for problem-solving.
Represent knowledge using logical and probabilistic models.
Develop and evaluate machine learning models.
Implement natural language processing techniques.
Understand the principles of robotics and autonomous systems.

|| Requirements

Proficiency in a programming language (Python preferred)
Basic understanding of algorithms and data structures

Familiarity with fundamental concepts in probability, statistics, and linear algebra

|| Requirements

Proficiency in a programming language (Python preferred)
Basic understanding of algorithms and data structures
Familiarity with fundamental concepts in probability, statistics, and linear algebra

This comprehensive Artificial Intelligence course delves deeply into the field of Natural Language Processing (NLP), starting with fundamental concepts and progressing to advanced techniques. Students will explore the basics of language models, text processing, and tokenization, understanding how machines interpret and generate human language. The course covers essential algorithms and models, including Bag-of-Words, TF-IDF, and word embeddings like Word2Vec and GloVe, which are crucial for semantic understanding. Moving to advanced NLP, the curriculum emphasizes sequence-to-sequence models, transformers, and attention mechanisms, highlighting their applications in machine translation, sentiment analysis, and conversational AI. Practical sessions involve building and fine-tuning models using deep learning frameworks such as TensorFlow and PyTorch. Additionally, the course addresses real-world challenges such as handling noisy data, dealing with biases, and ensuring ethical AI deployment. Through hands-on projects and case studies, students will gain expertise in creating sophisticated NLP applications, preparing them for careers in AI research and industry roles.

Natural Language Processing
Part I NLTK
What is NLP?
Typical NLP Tasks
Morphology
Sentence Segmentation & Tokenization
Pattern Matching with Regular Expression
Stemming, Lemmatization
Stop Words Removal (English)
Corpora/Corpus
Context Window – Bigram, Ngram
Applications of NLP
Introduction to the NLTK Library
Processing Raw Text
Regular Expression
Normalising Text
Processing Raw Text – Tokenise Sentences
String Processing with Regular Expression, Normalising Text
Extracting Features from Text
Bag-of-Words(BoW), TF-IDF
Similarity score Cosine similarity

Computer Vision
Image Formation
Sampling and Quantisation
Image Processing – flipping, cropping, rotating, scaling
Image statistics & Histogram
Spatial Resolution
Gray level/Intensity Resolution
Spatial Filtering
Convolution
Smoothing, Sharpening
Color Space Conversion &
Histogram
Thresholding for Binarization
Morphological Operations
Image Gradient
Bounding Box
Sobel’s Edge Detection Operator
Template Matching
Image Feature – Keypoint and Descriptor
Harris Corner Detector
Object Detection with HoG
Stream Video Processing with OpenCV

Advance NLP
"Use Logistic Regression,
Naive Bayes and Word vectors to implement Sentiment Analysis"
R-CNN
RNN
Encoder-Decoder
Transformer
Reformer
Embeddings
Information Extraction
LSTM
Attention
Named Entity Recognition
Transformers
HuggingFace
BERT
Text Generation
Named Entity Recognition
GRU
Siamese Network in TensorFlow
Self Attention Model
Advanced Machine Translation of Complete Sentences
Text Summarization

Prompt Engineering
Why Prompt Engineering?
ChatGPT
Few Standard Definitions:
Label, Logic
Model Parameters (LLM Parameters)
Basic Prompts and Prompt Formatting
Elements of a Prompt, Context
Task Specification
Constraints
General Tips for Designing Prompts:
Be Specific ,Keep it Concise
Be Contextually Aware
Test and Iterate
Prompt Engineering Use Cases
Information Extraction
Text Summarization
Question Answering
Code Generation
Text Classification
Prompt Engineering Techniques
N-shot Prompting
Zero-shot Prompting
Chain-of-Thought (CoT) Prompting
Generated Knowledge Prompting

Problem Identification and Definition
Define a clear problem statement or task that the AI project aims to address. This could involve tasks such as image classification, natural language processing, predictive analytics, etc.
Understand the scope, objectives, and constraints of the project.

Data Collection and Preprocessing
Identify relevant data sources needed to train and evaluate the AI model.
Clean and preprocess the data to handle missing values, outliers, and inconsistencies.
Perform exploratory data analysis (EDA) to understand the characteristics and distribution of the data.

Feature Engineering
Extract and select appropriate features from the data that are relevant to the problem statement.
Transform and engineer features to enhance the performance of the AI model.

Model Selection
Choose suitable AI models based on the nature of the problem and data.
Experiment with different algorithms such as supervised (e.g., regression, classification), unsupervised (e.g., clustering), or reinforcement learning based on project requirements.

Model Training and Evaluation
Split the dataset into training, validation, and test sets.
Train the selected AI model using the training data and validate its performance using the validation set.
Evaluate the model's performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score for classification; RMSE, MAE for regression).

Hyperparameter Tuning
Optimize model performance by tuning hyperparameters through techniques like grid search, random search, or Bayesian optimization.
Validate the tuned model to ensure improved performance.

Model Interpretation and Visualization
Interpret the AI model's predictions and understand its decision-making process.
Visualize model outputs and insights to communicate findings effectively.

Deployment and Integration
Prepare the AI model for deployment in a production environment.
Integrate the model into applications or systems through APIs or other deployment methods.
Ensure scalability, reliability, and efficiency of the deployed model.

Testing and Validation
Conduct rigorous testing to validate the AI model's functionality and performance in real-world scenarios.
Address any issues or bugs identified during testing phases.

Documentation and Reporting
Document the entire AI project process, including data sources, preprocessing steps, model selection criteria, and evaluation results.
Prepare comprehensive reports or presentations summarizing the project outcomes, insights gained, and recommendations for stakeholders.

Ethical Considerations
Consider ethical implications related to AI, such as fairness, transparency, privacy, and bias mitigation throughout the project lifecycle.
Implement measures to ensure responsible AI deployment and adherence to ethical guidelines.

Iterative Improvement
Iterate on the AI model based on feedback, new data, or evolving business requirements to enhance its performance and relevance over time.

By following a structured AI project process, students in an AI course gain practical experience in applying AI techniques to real-world problems, preparing them for careers in AI research, data science, and AI engineering. Each step in the process contributes to developing robust AI solutions that can drive innovation and decision-making across various industries.

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|| Skills to Master

Natural Language Processing (NLP):

Reinforcement Learning:

Deployment and Productionization:

Ethical and Responsible AI Development:

Computer Vision:

Software Development Practices:

|| Tools to Master

Docker

GITHUB

Jupyter Notebook

Google Cloud

Microsoft Azure

AWS

NLTK

Transformers

OpenCV

GIT

Google Colab

|| Scope of Artificial Intelligence Course

The scope of AI in India is broad and expanding rapidly across various sectors. Key areas where AI is expected to have a significant impact include:

Healthcare

Diagnostics and Imaging: AI can assist in early diagnosis and interpretation of medical images, enhancing accuracy and reducing human error.
Personalized Medicine: AI algorithms can analyze patient data to tailor treatments to individual needs.
Telemedicine: AI-powered chatbots and virtual assistants can provide initia consultations and triage, improving access to healthcare.

Agriculture

Precision Farming: AI tools can analyze weather patterns, soil conditions, and crop health to optimize farming practices.
Yield Prediction: AI can predict crop yields and assist in supply chain management.
Disease Detection: AI can identify plant diseases early, allowing for timely intervention.

Education

Personalized Learning: AI can create customized learning experiences for students based on their strengths and weaknesses.
Administrative Tasks: AI can automate administrative tasks, freeing up educators to focus more on teaching.
Tutoring Systems: Intelligent tutoring systems can provide additional support to students outside of the classroom.

Finance

Fraud Detection: AI can analyze transaction patterns to detect fraudulent activities.
Credit Scoring: AI can evaluate creditworthiness more accurately by analyzing a wide range of data points.
Algorithmic Trading: AI-driven algorithms can execute trades at optimal times, improving returns.

Retail and E-commerce

Personalized Recommendations: AI can analyze customer behavior to provide personalized product recommendations.
Inventory Management: AI can optimize inventory levels and reduce waste through predictive analytics. C
Customer Service: AI-powered chatbots can handle customer inquiries efficiently.

Manufacturing

Predictive Maintenance: AI can predict equipment failures before they occur, reducing downtime.
Quality Control: AI can enhance quality control by identifying defects in products more accurately.
Supply Chain Optimization: AI can improve supply chain efficiency by predicting demand and managingogistics.

Transportation

Autonomous Vehicles: AI is crucial for the development of self-driving cars and other autonomous transportation systems.
Traffic Management: AI can optimize traffic flow and reduce congestion in urban areas.
Logistics: AI can streamline logistics and delivery services, improving efficiency.

Energy

Smart Grids: AI can optimize energy distribution in smart grids, reducing waste and improving reliability.
Predictive Maintenance: AI can foresee issues in energy infrastructure, preventing outages and maintaining efficiency.
Renewable Energy: AI can enhance the efficiency of renewable energy sources like wind and solar by predicting weather patterns and adjusting operations accordingly.

Public Services

E-governance: AI can streamline government services, making them more accessible and efficient.
Citizen Services: AI chatbots can handle citizen inquiries and service requests, improving response times.
Security: AI can enhance surveillance and security measures, improving public safety.

Startups and Innovation

AI Startups: India is seeing a surge in AI startups across various sectors, contributing to innovation and job creation.
Incubation and Funding: Government initiatives and private investments are supporting AI research and development.

Government Initiatives

National AI Strategy: The Indian government has launched the National AI Strategy to promote the adoption and development of AI.
AI Research Institutes: Establishment of AI research centers and institutes to drive innovation.
Policy and Regulation: Developing frameworks for ethical AI use and data protection.

The scope of AI in India is vast and holds immense potential to transform industries, improve public services, and contribute to economic growth. With continued investment, research, and policy support, AI is set to play a pivotal role in India's development.

|| Artificial Intelligence Holds a Prominent Position In The Indian Job Market

In India, the landscape for artificial intelligence (AI) placement opportunities is rapidly expanding, reflecting the country's growing emphasis on technology and innovation. With a burgeoning ecosystem of startups, multinational corporations, and research institutions, there's a plethora of avenues for AI enthusiasts to explore. Major industries such as IT services, healthcare, finance, e-commerce, and manufacturing are actively integrating AI solutions, creating a high demand for skilled professionals. Companies like Tata Consultancy Services (TCS), Infosys, Wipro, Accenture, and IBM are consistently hiring AI specialists to develop cutting-edge applications in areas such as natural language processing, computer vision, robotics, and predictive analytics. Moreover, India's government initiatives like the National AI Portal and Atal Innovation Mission further bolster the AI ecosystem, offering grants, incubation support, and training programs. Additionally, academic institutions such as the Indian Institutes of Technology (IITs) and Indian Institutes of Information Technology (IIITs) are fostering AI talent through specialized courses and research opportunities. Overall, the AI placement landscape in India is vibrant, offering a diverse range of opportunities for individuals looking to make significant contributions to the field of artificial intelligence.

|| Empowering Your Career Transition From Learning To Leading

Megha Bhatt

Megha Bhatt, an ML Engineer at Cognizant, demonstrates prowess by leveraging unique tools such as Alteryx for advanced data blending and Google BigQuery for large-scale data analytics. Her adept use of these cutting-edge tools contributes to innovative and efficient data analysis.

Darshna Dave

Darshna Dave, excelling as a Data Analyst at Deepak Foundation post-IT institute, showcases expertise in unique tools such as KNIME for data analytics workflows, Apache Superset for interactive data visualization, and RapidMiner for advanced predictive analytics.

Shubham Ambike

Shubham Ambike, excelling as a Digital MIS Executive at Alois post-Business Analytics course, showcases expertise in tools like Microsoft Excel, Power BI, and Google Analytics. His adept use of these tools contributes to efficient data management and analysis. Congratulations on his placement.

Mehul Sirohi

Mehul Sirohi, excelling as a Data Associate at Numerator post learning Data Analytics course, skillfully employs unique tools such as Alteryx for data blending, Jupyter Notebooks for interactive data analysis, and Looker for intuitive data visualization. His mastery of these advanced tools contributes to Numerator's data processing success.

Pratik Shah

Pratik Shah excels in Data Processing at NielsenIQ after studying a Full-Stack Data science course from BIT. Proficient in tools like Excel, SQL, and Python, Pratik ensures precise and efficient data handling. Congratulations on his placement, which showcases his expertise in essential data processing tools.

|| Artificial Intelligence Career Option And Salary

In India, the job roles in the field of artificial intelligence (AI) vary widely based on specialization, experience, and industry. Here are some common AI job roles along with approximate salary ranges:

Machine Learning Engineer/ Data Scientist: Designing and implementing machine learning models, analyzing data, and developing algorithms.
Salary Range: ₹600,000 - ₹2,000,000 per annum.

AI Research Scientist: Conducting research in artificial intelligence, exploring new algorithms, and developing innovative AI solutions.
Salary Range: ₹800,000 - ₹3,000,000 per annum.

AI Software Developer: Writing code for AI applications, integrating AI components into software systems, and optimizing algorithms.
Salary Range: ₹400,000 - ₹1,500,000 per annum.

Natural Language Processing (NLP) Engineer: Building systems that understand and generate human language, working on tasks like text classification, sentiment analysis, and machine translation.
Salary Range: ₹600,000 - ₹2,000,000 per annum.

Computer Vision Engineer: Developing algorithms for analyzing and interpreting visual data, such as images and videos, for applications like object detection, facial recognition, and autonomous vehicles.
Salary Range: ₹600,000 - ₹2,000,000 per annum.

AI Product Manager: Overseeing the development of AI-powered products, defining product strategies, and collaborating with cross-functional teams.
Salary Range: ₹1,000,000 - ₹3,500,000 per annum.

AI Ethics Specialist: Addressing ethical considerations related to AI development and deployment, ensuring fairness, transparency, and accountability in AI systems.
Salary Range: ₹800,000 - ₹2,500,000 per annum.

AI Consultant: Providing advisory services to businesses on AI strategy, implementation, and optimization.
Salary Range: ₹800,000 - ₹3,000,000 per annum.

These salary ranges are approximate and can vary based on factors such as location, company size, educational background, and individual skills and expertise. Additionally, with the rapid growth of the AI industry in India, these figures may change over time.

|| Job Roles And Salary

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|| Some Prominent Companies In India That Use Artificial Intelligence

Many companies in India are leveraging artificial intelligence (AI) for various applications, ranging from improving customer service to optimizing supply chain management. Some notable companies that have incorporated AI into their operations include:

Tata Consultancy Services (TCS): TCS has developed an AI platform called ignio which automates and optimizes IT operations and business processes
Infosys: Infosys uses its AI platform, Nia, to help businesses forecast revenues, optimize supply chains, and understand customer behavior.
Wipro: Wipro’s AI platform, HOLMES, offers solutions for cognitive automation, robotics, and other AI-driven services to improve business processes.
HCL Technologies: HCL employs AI to enhance their IT and business services, focusing on areas like automation, analytics, and machine learning through their DRYiCE platform.
Reliance Industries: Reliance is using AI for various applications, including their telecommunications venture Jio, which uses AI for customer service and network optimization.
Flipkart: The e-commerce giant employs AI for improving customer recommendations, optimizing logistics, and enhancing search functionalities on its platform.
Ola: Ola uses AI to optimize routes, reduce waiting times, and improve the overall customer experience in their ride-hailing services.
Zomato: Zomato leverages AI to personalize user experiences, recommend dishes, and optimize delivery routes.
Swiggy: Swiggy uses AI to improve order allocation, predict delivery times, and enhance customer experience.
HDFC Bank: HDFC Bank utilizes AI for fraud detection, customer service through chatbots, and credit scoring.
ICICI Bank: ICICI Bank uses AI for personalized banking, virtual assistants, and fraud detection.
Airtel: Airtel uses AI to enhance network management, customer service, and predictive maintenance.

These companies are integrating AI to streamline operations, enhance customer service, and gain competitive advantages in their respective fields.

|| Top Hiring Companies

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|| Get Artificial Intelligence Certification

Three easy steps will unlock your Artificial Intelligence Certification:

Finish the online / offline course of Artificial Intelligence Course and the Assignment
Take on and successfully complete a number of industry-based Projects
Pass the Artificial Intelligence Certification exam

The certificate for this Artificial Intelligence n Certification will be sent to you through our learning management system, where you can also download it. Add a link to your certificate to your CV or LinkedIn profile.

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|| Certified Artificial Intelligence Training in Dahod

|| Choose Artificial Intelligence Course From BIT

|| What will I learn?

|| What will I learn?

|| Requirements

|| Requirements