vishal vaibhav
Bangalore/Online
Skills :
About :
vishal is a Data Science and Artificial Intelligence practitioner with over nine years of experience applying statistical modeling, machine learning, deep learning, and natural lan…
vishal is a Data Science and Artificial Intelligence practitioner with over nine years of experience applying statistical modeling, machine learning, deep learning, and natural language processing to solve complex analytical problems across diverse domains. My professional work spans supervised and unsupervised learning, time‑series forecasting, anomaly detection, and the development of scalable MLOps pipelines using cloud platforms such as AWS, Azure, and GCP.
My technical expertise includes a broad range of methods and tools—ranging from classical algorithms (Logistic Regression, Random Forests, XGBoost, SVMs) to modern deep learning architectures (CNNs, RNNs, LSTMs) and advanced NLP techniques using BERT, Sentence Transformers, and vector‑based retrieval systems. I also bring hands‑on experience with Python, FastAPI, Pandas, scikit‑learn, and cloud‑native ML services, along with practical knowledge of GenAI frameworks, RAG pipelines, and vector databases.
As an instructor, I emphasize conceptual clarity, mathematical intuition, and real‑world applicability. My teaching approach integrates research‑backed methodologies, industry‑grade workflows, and structured problem‑solving to help learners build strong foundations in AI and data science. I am committed to fostering analytical thinking and guiding students toward mastery of both fundamental principles and modern AI techniques.
vishal vaibhav
Bangalore/Online
Skills :
vishal is a Data Science and Artificial Intelligence practitioner with over nine years of experience applying statistical modeling, machine learning, deep learning, and natural lan...
vishal is a Data Science and Artificial Intelligence practitioner with over nine years of experience applying statistical modeling, machine learning, deep learning, and natural language processing to solve complex analytical problems across diverse domains. My professional work spans supervised and unsupervised learning, time‑series forecasting, anomaly detection, and the development of scalable MLOps pipelines using cloud platforms such as AWS, Azure, and GCP.
My technical expertise includes a broad range of methods and tools—ranging from classical algorithms (Logistic Regression, Random Forests, XGBoost, SVMs) to modern deep learning architectures (CNNs, RNNs, LSTMs) and advanced NLP techniques using BERT, Sentence Transformers, and vector‑based retrieval systems. I also bring hands‑on experience with Python, FastAPI, Pandas, scikit‑learn, and cloud‑native ML services, along with practical knowledge of GenAI frameworks, RAG pipelines, and vector databases.
As an instructor, I emphasize conceptual clarity, mathematical intuition, and real‑world applicability. My teaching approach integrates research‑backed methodologies, industry‑grade workflows, and structured problem‑solving to help learners build strong foundations in AI and data science. I am committed to fostering analytical thinking and guiding students toward mastery of both fundamental principles and modern AI techniques.
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Courses by vishal vaibhav
Online and Offline
5 Month
English, Hindi
Bangalore, Urban Lotus Apartment
1000 INR
Week Days / Weekends
What You Will Learn
This course covers essential topics in Data Science and Artificial Intelligence. You will start with the foundations of data science and AI, learning key concepts and terminology.
- Foundations of Data Science & AI: Understand the basic principles and applications of data science and AI.
- Python Programming for Data Analysis: Learn Python coding for data manipulation and analysis.
- Data Wrangling with NumPy & Pandas: Practice data cleaning and preparation using popular Python libraries.
- Exploratory Data Analysis & Visualization: Explore data sets and create visual representations to uncover insights.
- Core Machine Learning Algorithms: Study fundamental algorithms used in machine learning.
- Unsupervised Learning & Clustering Techniques: Discover methods for grouping data without labeled outcomes.
- Feature Engineering & Model Optimization: Learn how to select and modify features to improve model performance.
- Deep Learning & Neural Networks: Understand the architecture and functioning of neural networks.
- Advanced NLP & Transformer Models: Explore natural language processing techniques and advanced models.
- Generative AI & LLM Applications: Learn about applications of generative AI and large language models.
- Time Series Analysis & Forecasting: Analyze time-dependent data and make forecasts.
- Data Engineering Essentials & SQL: Gain knowledge in data engineering concepts and SQL for database management.
- Cloud Platforms for Data Science (AWS, Azure, GCP): Understand how to use cloud services for data science projects.
- MLOps & End-to-End Model Deployment: Learn how to deploy machine learning models in a production environment.
- Business Analytics, Storytelling & Dashboards: Develop skills in presenting data insights effectively.
- Capstone Projects (ML, NLP/GenAI, MLOps): Apply your learning in practical projects to solidify your understanding.
- Career Preparation & Interview Readiness: Prepare for job opportunities in data science.
The course progresses from basic concepts to advanced techniques, ensuring a comprehensive understanding of data science and AI.