Suvo Data Science,LLM,MLOPS,
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This course merges interactive lectures with hands-on projects, gradually transitioning from basic Python programming to advanced Machine Learning and GenAI techniques. Participants engage in real-world tasks, leveraging peer collaboration and continuous feedback to ensure deep understanding and practical skills. Participants will master Python and data science tools, gaining proficiency in machine learning algorithms and completing a capstone project that prepares them for advanced data science roles.

Course Outline:

Introduction to Data Science & Machine Learning

Basics of Data Science and Python programming
Data manipulation with Python
Data Analysis using Python

Data cleaning and Exploratory Data Analysis (EDA) with Pandas and Numpy
Advanced Data Handling

Data visualization techniques and project work
Statistical Foundations in Data Science

Probability, statistics, and statistical analysis using Python
Machine Learning Fundamentals

Introduction to supervised and unsupervised learning, including classification and regression techniques
Deep Learning Essentials

Introduction to neural networks, including ANN, CNN, and RNN
Practical applications and training of deep neural networks
Advanced Machine Learning Models

PCA, clustering, ensemble models, and hyperparameter tuning
Model Evaluation and Advanced Topics

Model optimization, validation, and introduction to advanced topics like reinforcement learning and deep learning
Introduction to Generative AI and LLMs

Overview of GenAI, LLMs, architectures, and applications
Prompt Engineering and Application Development

Crafting effective prompts, developing LLM applications
Deployment of Models

Using cloud platforms like AWS and Azure for model deployment and management

Subjects

Deep Learning Expert
Data Science and Machine Learning Expert
Python for Data science, Machine Learning and Artificial Intelligence Expert
GenAI Expert

Experience

Lead Data Scientist -III (Mar, 2019–Present) at Tech Lead in a product based company

Experienced Data Science Team Lead with over four years in product development, specializing in IoT, Healthcare, Automotive, and FMCG sectors. Currently focused on building and implementing Large Language Models (LLMs). Proven expertise in leading cross-functional teams, managing full project lifecycles, and driving innovation in data science.

Education

B.Tech (Aug, 2016–Feb, 2020) from IEM kolkata

Fee details

₹573–5,000/hour (US$6.81–59.46/hour)

Basic Subject: 573/hour - For introductory topics.
Core Subject: 682/hour - For advanced topics requiring specialized knowledge.
Extended Engagement: 3,440/ 2.5 hour - For intensive or long-duration sessions.
Bulk Rate: 5000/ 5 hour- Fixed price for substantial long-term or bulk engagements.

Courses offered

Data Science from Scratch to Advanced for Everyone
- USDFREE
- Duration: 4 Months
- Delivery mode: Online
- Group size: Individual
- Instruction language: English, Hindi, Bengali
- Certificate provided: No
Course Outline:

Introduction to Data Science & Machine Learning

Basics of Data Science and Python programming
Data manipulation with Python
Data Analysis using Python

Data cleaning and Exploratory Data Analysis (EDA) with Pandas and Numpy
Advanced Data Handling

Data visualization techniques and project work
Statistical Foundations in Data Science

Probability, statistics, and statistical analysis using Python
Machine Learning Fundamentals

Introduction to supervised and unsupervised learning, including classification and regression techniques
Deep Learning Essentials

Introduction to neural networks, including ANN, CNN, and RNN
Practical applications and training of deep neural networks
Advanced Machine Learning Models

PCA, clustering, ensemble models, and hyperparameter tuning
Model Evaluation and Advanced Topics

Model optimization, validation, and introduction to advanced topics like reinforcement learning and deep learning
Introduction to Generative AI and LLMs

Overview of GenAI, LLMs, architectures, and applications
Prompt Engineering and Application Development

Crafting effective prompts, developing LLM applications
Deployment of Models

Using cloud platforms like AWS and Azure for model deployment and management