Best Data Science And Artificial Intelligence With Gen AI Course
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Data Science Course Curriculum (Syllabus)
- INTRODUCTION TO PYTHON
- DIFFERENT MODES IN PYTHON
- VARIABLES IN THE PYTHON
- PYTHON OPERATORS AND OPERANDS
- PYTHON CONDITIONAL STATEMENTS
- PYTHON LOOPS
- LEARNING PYTHON STRINGS
- SEQUENCE IN PYTHON
- PYTHON LISTS
- PYTHON TUPLE
- PYTHON SETS
- PYTHON DICTIONSRY
- PYTHON FUNCTIONS
- PYTHON MODULES
- PYTHON DATE AND TIME
- READING AND WRITING FILES
- PYTHON OS MODULES
- PYTHON EXCEPTION HANDLING
- PYTHON ITERATORS
- PYTHON GENERATORS
- PYTHON DECORATORS
- PYTHON CLASS AND OBJET(OOP)
- OOP PRINCIPLES
- GARBAGE COLLECTION
- INHERITANCE
- MULTIPLE INHERITANCE
- OPERATOR OVERLOADING
- POLYMORPHISM
- ABSTRACTION
- ENCAPSULATION
- PYTHON REGULAR EXPRESSIONS
- UNDERSTANDING THE DATA
- PROBABILITY DISTRIBUTIONS
- SAMPLING DISTRIBUTIONS
- HYPOTHESIS TESTING
- ASSOCIATION BETWEEN CATEGORICAL VARIABLES
- ANOVA ANALYSIS
DATA SCIENCE & AI
MACHINE LEARNING
- WHAT EXACTLY DATA SCIENCE IS
- ARTIFICIAL INTELLIGENCE VS DATA SCIENCE VS BIG DATA
- DATA ANALYST VS DATA SCIENTIST VS BIG DATA ENGINEER VS MACHINE LEARNING ENGINEER
- WHY DATASCIENTISTS ARE IN DEMAND
- WHAT IS DATA PRODUCT
- NEED FOR DATASCIENTIST
- FOUNDATIONS OF DATASCIENCE
- DATA SCIENCE PROJECT LIFE CYCLE AND STAGES
- WHAT IS BUSINESS INTELLIGENCE
- WHAT IS DATA ANALYSIS
- WHAT IS DATA MINING
- WHAT IS MACHINE LEARNING
- ANALYTICS VS DATACIENCE
- ANALYTICS PROJECT LIFE CYCLE
- BIG DATA
- DATA SCIENCE DEEP DIVE
- BASICS OF DATA CATEGORIZATION
- TYPES OF DATA
- DATA COLLECTION TYPES
- DIFFERENT CONCEPTS OF DATA
- FORMS OF DATA AND SOURCES
- DATA FORMATS
- DATA QUANTITY
- DATA QUALITY
- DATA TRANSFORMATION
- FILE FORMAT CONVERSIONS
- DATA QUALITY AND CHANGES
- DATA QUALITY ISSUES
- DATA QUALITY STORY
- WHAT IS DATA ARCHITECTURE
- COMPONENTS OF DATA ARCHITECTURE
- OLTP VS OLAP
- HOW IS DATA STORED
- PANDAS
- NUMPY
- SKLEARN
- SCIPY
- PLOTLY
- MATPLOTLIB AND SEABORN
- KERAS
- TENSORFLOW
- PYTORCH
- NLTK
- SPACY
- MACHINE LEARNING FUNDAMENTALS
- UNDERSTANDING SUPERVISED AND UNSUPERVISED LEARNING TECHNIQUES
- CLUSTERING
- IMPLEMENTATION OF ASSOCIATION RULE
- UNDERSTANDING THE PROCESS FLOW OF SUPERVISED LEARNING TECHNIQUE
- LINEAR REGRESSION
- MULTI LINEAR REGRESSION
- POLYNOMIAL LINEAR REGRESSION
- LOGISTIC REGRESSION
- DECISION TREE
- RANDOM FOREST
- SUPPORT VECTOR MACHINES
- K NEAREST NEIGHBOUR
- XG BOOST
- ADA BOOST
- BAGGING CLASSIFIER
- VOTING CLASSIFIER
- NAIVE BAYS CLASSIFIER
- FEATURE ENGINEERING
- TEXT MINING
- SENTIMENT ANALYSIS
- TIME SERIES ANALYSIS
- NATURAL LANGUAGE PROCESSING
- RECOMMENDATION SYSTEMS
- COMPUTER VISION
- DEEP LEARNING
PYSPARK IN MACHINE LEARNING
- STUDYING VARIOUS ALGORITHMS THEORETICALLY AND PROGRAMATICALLY
- APPLYING DIFFERENT ALGORITHMS TO DIFFERENT DATASETS
- HOW TO SELECT THE RIGHT DATA
- FEATURE SELECTION TECHNIQUES
- PREPROCESSING INTRODUCTION
- NORMALIZATION TECHNIQUES
- SCALING TECHNIQUES
- REGULARISATION TECHNIQUES
- STANDARDISATION TECHNIQUES
- PRINCIPLE COMPONENT ANALYSIS
- SINGULAR VALUE DECOMPOSITION
- LINEAR DISCRIMINATE ANALYSIS
GRADIENT DESCENT CONCEPTS
- INTRODUCTION TO MODEL TUNING
- PARAMETER TUNING GRID SEARCHCV
- SELECTING THE BEST ALGORITHM
DEEP LEARNING
- MACHINE LEARNING VS DEEP LEARNING
- BASICS OF BIOLOGICAL NEURON
- BASICS OF ARTIFICIAL NEURON
- PERCEPTRON
- WHAT IS NEURON
- WHAT IS INPUT LAYER
- WHAT IS HIDDEN LAYER
- WHAT IS OUTPUT LAYER
- WHAT IS FULLY CONNECTED NETWORK
- LINERA FUNCTIONS
- NON LINEAR FUNCTIONS
- ACTIVATION FUNCTIONS
- LOSS FUNCTIONS
- OPTIMIZERS
- GRADIENT
- GRADIENT DESCENT
- STOCHASTIC GRADIENT DESCENT
- COST FUNCTION
- PROBLEMS OF GRADIENT DESCENT
- FORWARD PROPAGATION
- BACKWORD PROPAGATION
- HOW TO TRAIN NEURAL NETWORK
- HOW TO VALIDATE A NEURAL NETWORK
- CONCEPTS OF OVERFITTING AND UNDERFITTING
- ARTIFICIALNEURAL NETWORK
- CONVOLUTION NEUAL NETWORK
- RECORRUNT NEURAL NETWORK
LSTM
Gen AI & Prompt Engineering
- INTRODUCTION
- ALGORITHMS
- DATA AUGMENTATION TECHNIQUES
- INTRODUCTION
- TEXT NORMALIZATION,
- EDIT DISTANT
- LANGUAGE MODELLING WITH N GRAMS
- NAIVE BAYES CLASSIFICATION AND SENTIMENT(NLP + ML)
- LOGISTIC REGRESSION(NLP + ML)
- VECTOR SEMANTICS AND EMBEDDINGS
- NEURAL NETS AND NEURAL LANGUAGE MODELS(NLP + DL)
- PART-OF-SPEECH TAGGING
- SEQUENCE PROCESSING WITH RECURRENT NETWORKS
- ENCODER-DECODER MODELS, ATTENTION, AND CONTEXTUAL EMBEDDINGS
- CONSTITUENCY GRAMMARS
- CONSTITUENCY PARSING
- STATISTICAL CONSTITUENCY PARSING
- DEPENDENCY PARSING
- LOGICAL REPRESENTATIONS OF SENTENCE MEANING
- COMPUTATIONAL SEMANTICS AND SEMANTIC PARSING
- INFORMATION EXTRACTION
- WORD SENSES AND WORDNET
- SEMANTIC ROLE LABELING AND ARGUMENT STRUCTURE
- LEXICONS FOR SENTIMENT, AFFECT, AND CONNOTATION
- COREFERENCE RESOLUTION
- DISCOURSE COHERENCE
- SUMMARIZATION
- QUESTION ANSWERING
- DIALOG SYSTEMS AND CHATBOTS
- PHONETICS
- SPEECH PROCESSING
- HIDDEN MARKOV MODELS
- LATENT DIRCHLET ALLOCATION
- IMAGE ENHANCEMENT
- IMAGE DENOISING
- TRANSFORMATIONS
- FILTERING, FOURIER AND WAVELET TRANSFORMS AND IMAGE COMPRESSION
- COLOR VISION
- FEATURE EXTRACTION
- POSE ESTIMATION
- REGISTRATION
- Introduction to Generative AI.
- AI vs ML vs DL vs NLP vs Generative AI.
- Generative AI principles.
- What is the role of ML in GenAI.
- Ethical considerations.
- Generative AI applications.
- Generative AI use cases.
- Auto encoders.
- VAE’s and applications.
- GANs and it’s applications.
- Different types of GANs and applications.
- Different types of Language models
- Applications of Language models
- Transformers and its architecture
- BERT, RoBERTa, GPT variations
- Applications of transformer models
- Generative AI lifecycle
- What is RLHF
- LLM pre-training and scaling
- Different Fine-Tuning techniques
- LLMs Embedding
- What is Chunking
- What is the use of chunking the document
- What are the traditional effective chunking techniques
- What are the problems and limitations with traditional chunking techniques?
- How to overcome the limitations of Traditional chunking
- Advanced Chunking Techniques:
Character Splitting
2. Recursive Character Splitting
3. Document based Chunking
4. Semantic Chunking
5. Agentic Chunking
- What is RAG
- What are the main components of RAG
- High level architecture of RAG
- How to Build RAG using external data sources
- Advanced RAG
- What is Lang-chain
- What are the core concepts of Lang-chain
- Components of Lang-chain
- How to use Lang-chain agents
- LlamaIndex
- What are Vector Databases
- Why do we prefer Vector Databases over Traditional Databases
- Different Types of Vector Databases: OpenSource and Close Source
- OpenSource: Chroma DB, Weaviate, Faiss, Qdrant
- Close-Source Vector Databases: Pinecone, ArangoDB, Cloud-Based
Solutions
- Supervised Finetuning
- Repurposing-Feature Extraction
- Advanced techniques in Supervised Finetuning -PEFT -LoRA, QLoRA
- Text based LLMs: Automatic Evaluation: BULE Score, ROUGE Score, METEOR, BERT Score.
- Human Evaluation: Coherence, Factuality, Originality, Engagement
- Image based LLMs:
Automatic Evaluation: Pixel-level metrics, FID (Frechet Inception
Distance), IS (Inception Score), Perceptual Quality Metrics,
Diversity Metrics. - Human Evaluation: Photorealism, Style, Creativity, Cohesiveness
- Audio generation LLMs: Automatic Evaluation: FAD (Frechet Audio Distance), IS (Inception Score), Perceptual Quality Metrics – PAQM, PAQM – SNR (Signal-to-Noise Ratio), PAQM – PESQ (Perceptual Evaluation of Speech
Quality) - Human Evaluation: Perceptual Quality – PQ, PQ- Naturalness, PQFidelity, PQ- Musicality, Task Specific Evaluation.
- Video Generation LLMs: Automatic Evaluation: FVD (Frechet Video Distance), Inception Score(IS), Perceptual Quality Metrics, Motion Based Metrics – Optical Flow Error, Content-Specific Metrics.
Human Evaluation: Visual Quality, Temporal Coherence, Content Fidelit.
- Model Deployment and Management
- Scalability and Performance Optimization
- Security and Privacy
- Monitoring and Logging
- Cost Optimization
- Model Interpretability and Explainability.
- Amazon Bedrock, Azure OpenAI
- ChatGPT, Gemini, Copilot
- What is Prompt Engineering
- What are the different principles of Prompt Engineering
- Types of Different Prompt Engineering Techniques
- How to Craft effective prompts to the LLMs
- Priming Prompt
- Prompt Decomposition
- what is prompt
- what is response
- Introduction to chat-gpt
- Other AI tools similar to chat-gpt like bard, bing
- Major tasks in prompt engineering as below.
-> prompt to Text content
-> prompt to Image generation
-> prompt to Audio generation
-> prompt to video generation
-> prompt to task execution. - many tools available in market, what does prompt
engineer has to do ?
prompt engineering challenges.
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Data Science Course Training Highlights
Learn data science with practical training and real-world skills.
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Job opportunities and Career Paths in Data Science Technology
Data Science is one of the most exciting and fastest-growing fields today. It combines skills like analyzing data, creating smart solutions with AI, and solving real-world problems. With businesses depending more on data, there is a huge demand for Data Science experts.
Popular career paths include Data Analyst, Data Scientist, Machine Learning Engineer, and AI Specialist. These jobs are highly paid and have great growth opportunities in industries like healthcare, finance, IT, and more.
Join our course to learn the latest tools like Python, R, and SQL, and get hands-on experience with projects. Start your journey to a successful career in Data Science today!
Here are some of the leading data science careers you can pursue with an advanced course:
1. MACHINE LEARNING ENGINEER
2. DEEP LEARNING ENGINEER
3. NLP ENGINEER
4. AI RESEARCH SCIENTIST
5. AI ENGINEER
6. GENAI ENGINEER
7. PROMPT ENGINEER
8. JUNIOR DATA SCIENTIST
9. SENIOR DATA SCIENTIST
10. ML LEAD MANAGER
11. AI SOLUTION ARCHITECT
12. DATA ANALYST
13. BUSINESS ANALYST
14. OPENCV ENGINEER
15. GENAI Product Manager
16. AI Ethics Specialist
17. AI Content Creator
18. GENAI RESEARCH SCIENTIST
Data Science salaries
Machine Learning Engineer
Fresher Salary: ₹4,00,000 – ₹8,00,000 per year
Experienced Salary: ₹12,00,000 – ₹20,00,000+ per year
Deep Learning Engineer
Fresher Salary: ₹4,00,000 – ₹8,00,000 per year
Experienced Salary: ₹12,00,000 – ₹20,00,000+ per year
Natural Language Processing (NLP) Engineer
Fresher Salary: ₹5,00,000 – ₹8,00,000 per year
Experienced Salary: ₹12,00,000 – ₹20,00,000+ per year
AI Research Scientist
Fresher Salary: ₹4,50,000 – ₹9,00,000 per year
Experienced Salary: ₹15,00,000 – ₹35,00,000+ per year
AI Engineer
Fresher Salary: ₹4,50,000 – ₹9,00,000 per year
Experienced Salary: ₹15,00,000 – ₹35,00,000+ per year
Generative AI Engineer
Fresher Salary: ₹5,00,000 – ₹8,00,000 per year
Experienced Salary: ₹15,00,000 – ₹30,00,000+ per year
Prompt Engineer
Fresher Salary: ₹4,00,000 – ₹8,00,000 per year
Experienced Salary: ₹10,00,000 – ₹20,00,000+ per year
Junior Data Scientist
Fresher Salary: ₹5,00,000 – ₹8,00,000 per year
Experienced Salary: ₹12,00,000 – ₹20,00,000+ per year
Data Analyst
Fresher Salary: ₹3,00,000 – ₹6,00,000 per year
Experienced Salary: ₹6,00,000 – ₹12,00,000 per year
Business Analyst
Fresher Salary: ₹4,00,000 – ₹8,00,000 per year
Experienced Salary: ₹8,00,000 – ₹16,00,000 per year
OpenCV Engineer
Fresher Salary: ₹4,00,000 – ₹8,00,000 per year
Experienced Salary: ₹8,00,000 – ₹16,00,000 per year
GenAI Research Scientist
Fresher Salary: ₹5,00,000 – ₹8,00,000 per year
Experienced Salary: ₹15,00,000 – ₹30,00,000+ per year
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Data Science course overview
The Data Science Course provides a comprehensive pathway for individuals eager to embark on a rewarding career in the field of data science. It covers foundational and advanced concepts like data analysis, machine learning, and data visualization, enabling learners to make sense of complex datasets. Through hands-on projects and real-world applications, participants gain practical skills to analyze, interpret, and present data effectively.
This course is designed to cater to a wide range of learners, from complete beginners to experienced professionals looking to upskill. By the end of the program, participants will have the knowledge and confidence to apply for data science roles across industries, becoming invaluable contributors to data-driven decision-making processes.
Who Can Learn?
- Aspiring Data Scientists: Individuals aiming for roles like data analyst, data scientist, or machine learning engineer.
- Beginners: Those new to the field can start without prior experience, making it an ideal entry point.
- IT Professionals: System administrators, network professionals, or IT support staff looking to pivot to data science.
- Data Enthusiasts: Passionate individuals eager to explore data analytics, predictive modeling, and more.
- Career Changers: Professionals from diverse fields seeking to transition into the rapidly growing data science domain.
Prerequisites
While this course is beginner-friendly, having some familiarity with basic mathematics and programming can be beneficial. Knowledge of Python or SQL is advantageous but not mandatory, as these tools will be introduced during the course. A willingness to learn and an analytical mindset are the most critical prerequisites for success in this field.
Qualifications and Skills Needed for a Data Scientist
To excel as a data scientist, learners should develop the following:
- Technical Skills: Proficiency in programming languages like Python, R, or SQL; expertise in data analysis and visualization tools; and understanding of machine learning algorithms.
- Mathematical Knowledge: A strong foundation in statistics, linear algebra, and probability is essential.
- Analytical Thinking: The ability to interpret data trends and derive actionable insights.
- Communication Skills: Effectively presenting findings to technical and non-technical stakeholders.
- Problem-Solving Ability: Addressing complex business challenges using data-driven strategies.
This course equips learners with these qualifications and skills, ensuring they are ready to meet the demands of data science roles in the modern workplace.
Data Science Trainer
Data Science Expert & Lead Instructor
An analytical & consulting professional with thought leadership, business acumen and technical skills leaving a mark of excellence in industries of Technology with over 10+ years of experience: GENAI/ Data Science/ Analytic/ Consulting/ Result-oriented professional with proven record of achievement in conceiving & implementing recommendations to drive revenue.
Thought leadership in area of GENAI, Data Science, Prompt Engineering and Building New Services from Data.
Proficiency in building Technical Skills for Professionals, Students in GENAI and Data Science, and Cloud Computing like AWS AZURE & GCP possess hands-on experience in conducting Services.
Experienced in formulating strategies, conducting market analysis related to sales & product performance, customer satisfaction, market sizing, opportunity analysis & competitive intelligence and recommending solutions to the clients that helps in reaching out to unexplored market for business expansion and improving their performance.
Evangelize analytics in various forums and presented technical Skills in Python, Statistics & Probability, Machine learning, Deep Learning, Computer Vision, NLP, GENAI, LLM's, Prompt Engineering and AWS services such as IAM, S3, EC2, VPC, ELB, AMI, SNS, CLOUD WATCH & CLOUD TRAIL, RDS, DYNAMODB, REDSHIFT, VPC, LAMBDA, AMAZON SAGEMAKER, EMR, KINESIS, ATHENA, KUBERNETES & KUBE FLOW, AMAZON REKOGNITION, AMAZON POLY, AMAZON TRANSLATE……
An enterprising leader with enriched interpersonal, analytical, troubleshooting and team management skills including attrition control & performance excellence; acquired knowledge & thought leadership.
Experienced with deploying machine learning & deep learning models in real time locally and expertise with edge devices, statistical modelling, data analysis, feature engineering & critical thinking
D S Prasad
10+ Years Experience
Worked For: Multiple MNC's companies
Currently working : Corporate Trainer For MNC's data science and GENAI Prompt Engineering
Data Science course Modes
Choose the learning mode that suits you best: Online, Offline, or Video Course
Classroom Training
Online Training
Video Course
Data Science Course Certification
This Data Science certification help improve job chances by showing your skills in areas like programming, machine learning, and data analysis. They make you stand out to employers, prove your abilities, and open up more job opportunities in the field.
Data Science Students Reviews
WHEN I WAS LOOKING FOR CAREER IN DATA SCIENCE I WAS CLUELESS, AFTER ATTENDING MULTIPLE DEMO SESSIONS I REALIZED THAT, THE WAY OF PRASAD SIR’s TEACHING WAS VERY EASY AND ITS CLEAR TO UNDERSTAND HIS CONCEPT, AFTER COURSE COMPLETION WHEN GOT REALTIME TASKS, I JUST FOLLOWED TRAINER SIR's CLASSES AND HAD SOLUTION FOR IT AND I TOOK RESUME AND INTERVIEW GUIDANCE WHICH FETCHED ME INTO DATA SCIENCE IN TCS
I WAS FROM COMPUTER SCIENCE BACKGROUND I HAD A FEAR OF PROGRAMMING LANGUAGE AND HAD ZERO LEVEL CONFIDENCE WHETHER I CAN GRAB AN OPPORTUNITY IN IT INDUSTRY BUT ONCE I TOOK COURSE FROM PRASAD SIR’s AND COMPLETED 6 MONTHS INTERNSHIP, DURING TRAINING RELATIME TASKS FROM DIFFERENT MNC's WAS GAVE CONFIDANCE IN SOLVING PROBLEM STATEMENTS, IT HAD TAKEN MY CAREER TO NEXT LEVEL AND LANDED ME INTO A MNC.
AN EXTREMELY PROFESSIONAL COURSE, AS A COMPUTER ENGINEER, I CAN ASSURE THAT THE DYNAMICS OF THIS COURSE ARE SIMILAR IN MANY ASPECTS TO CORPORATE TRAINING. THE INITIAL SECTIONS COVER EXTREMELY INTRESTING AND COVERS THEORITICALLY AND INTUITIVE WAY OF PROGRAMMING EXPLANATIONS AND STATE OF ART MATERIAL. PIN TO PIN EXPLANATION WAS AWSOME, REAL TIME ASSIAGNMENTS GAVE ME CONFIDANCE IN GETTING MORE INTERVIEW OPPORTUNITIES FINALLY GOT SELECTION.
I FIND THIS PROGRAM TO BE ABSOLUTELY EXTRAORDINARY AND ESSENTIAL FOR ACQUIRING THE NECESSARY KNOWLEDGE THAT TRULY ACTIONABLE TRAINING SHOULD HAVE THIS PROGRAM IS INTENDED TO EDUCATE WITH HARDLY ANY PROFIT MOTIVE AND I WILL BECOME AN APOSTLE OF GENERATIVE AI AND AI ACCELERA, MORE IMPORTANT THING IS NOT ONLY PROTOTYPE PROJECTS BUT ONCE COURSE COMPLETE THE REAL TIME ASSIAGMENT FROM DIFFERENT MNC's WILL GIVE REALTIME KNOWLEDGE AND IT IS MORE HELPFUL DURING INTERVIEW PROCESS.
I CAN CONFIDENTLY SAYS THAT THIS COURSE IS TRULY EXCEPTIONAL. IT IS STRUCTURED IN A WAY THAT STEP BY STEP CONCEPTS AND CHAIN LINKED.THIS COURSE STANDS OUT BECAUSE IT DELVES INTO TOPICS THAT ARE OFTEN OVERLOOKED. JUST FOLLOWED DAILY LIVE CLASSES AND MATERIAL AND PROGRAMMING FILES AND BUSINESS KNOWLEDGE FROM TRAINER SIR's. EVERY DAY TASKS WAS ONE OF THE FINEST THING THAT FOLLOWED BY TRAINER SIR AND WE MUST AND SHOULD RECOLLECT AND DEFINITELY WE HAVE TO WORK ON THE TASKS ON THE DAY ITSELF WILL GAIN GOOD PROGRAMMING KNOWLEDGE.
THE TRAINING I RECEIVED WAS ABSOLUTELY TOP-NOTCH. THE INSTRUCTOR WAS HIGHLY KNOWLEDGEABLE AND ENSURED THAT EVERY CONCEPT WAS EXPLAINED IN A SIMPLE AND ENGAGING WAY. THE HANDS-ON PROJECTS WERE CRUCIAL IN HELPING ME APPLY WHAT I LEARNED. THANKS TO THE TRAINER SUPPORT, I SECURED A JOB AT A IDRBT RIGHT AFTER COMPLETING THE COURSE. IT IS ONE BIGGEST BETTER LEARNING EXPERIENCE.
THIS DATA SCIENCE TRAINING WAS AN EYE-OPENER. THE CURRICULUM WAS COMPREHENSIVE, COVERING EVERYTHING FROM PYTHON TO PROMPT ENGINEERING. THE BEST PART WAS THE REAL-WORLD PROJECTS THAT GAVE ME THE PRACTICAL EXPERIENCE I NEEDED. THANKS TO THE DEDICATED CAREER SERVICES TEAM, I LANDED AN AMAZING ROLE AS A ML ENGINEER AT STARTUPUP COMPANY. HIGHLY RECOMMEND THIS PROGRAM TO ANYONE LOOKING TO BREAK INTO DATA SCIENCE AND GENAI
I HAD ALWAYS BEEN INTERESTED IN AI, BUT I AM FROM NON-CODING BACKGROUND INITIALLY I WAS LITTLE HURRY REGARDING TASKS PROVIDED BY TRAINER, I FOCUSED MORE ON THIS PYTHON PROGRAMMING AND FINALLY THIS GENAI COURSE TRULY OPENED MY EYES TO ITS POTENTIAL, ESPECIALLY IN DATA SCIENCE. THE CURRICULUM COVERED GENERATIVE MODELS IN-DEPTH AND INCLUDED HANDS-ON EXPERIENCE WORKING WITH ADVANCED ALGORITHMS LIKE GANS AND TRANSFORMERS. THE TRAINER WAS PASSIONATE AND SHARED THEIR REAL-WORLD EXPERIENCES, WHICH HELPED A LOT. I AM NOW WORKING AS A GENAI WITH A COMPANY.
