Data Science

Discover the Patterns, Create the Future with Data Science.

About This Course

Our Data Science course is designed to provide a comprehensive foundation in data analysis, machine learning, and artificial intelligence. You’ll learn key programming languages like Python, as well as tools for handling big data, cloud computing, and statistical modeling. The course offers practical, hands-on projects that simulate real-world applications, giving you the experience needed to succeed in the industry.

Whether you're a beginner or an experienced professional looking to advance your skills, this course covers everything from data visualization to deep learning. With expert instruction and flexible learning options, you'll be prepared to take on roles like Data Scientist, Machine Learning Engineer, or AI Specialist and thrive in the fast-growing data-driven economy.

STUDY, CODE, DEPLOY, UPSKILL

Learning Objectives

Master Key Programming Languages:
Understand Statistical Analysis
Apply Machine Learning Techniques
Build Real-World Projects

Syllabus Covered

Data Types, Conditional & control statements, File Handlings, OOPS,Data Structures including graph Theory
NUMPY,PANDAS, PANDASAI, MATPLOTLIB
Linear Algebra, Vectors, Matrics, SVD, Calculas, Chain Rules, Gradient Ascent & Desent, Minima & Mixima Functions, Optimazation theory Introduction to Stats, Descriptive & Infertial Stats
Basics, Bayes theorem, Probability & distribution, Bayes Data Analysis & Algorithms
Regression Models, Classification, Clustering, Recommendation Systems, Features selection, PCA, PCA for Regression, Probablites PCA, Dimenstionality Reducing Methods
Introduction to Image Processing, OPEN CV, Neural Networks, ANN, CNN, RNN, LSTM, Transformers, Transformers for OPEN CV, Object Detection and Tracking, Diffusion Modelling, DALL-E2, Object Detection without Labelled Data, Control Net
Introduction to NLP, Understanding of characters, Converting words to Numericals, TF IDF, Bag of Words, Embedding, BERT Arch, ChatGPT Arch, Build Custom ChatGPT, Large language Modelling, DV using Text data
Introduction to MLOPS, Levels of MLOPS, Model Packing and Testing of Application, Model validations, deployment, Strategies, MLOPS Frameworks, like KubeFlow, MLFlow, GenML, Model Observliblity-Model, data Driff, Root cause Analysis, Model Metrics & Dashboard Grefona, permet house, Model Expandability, model Fairness
Understanding Transformers and GPT
Training Large Language Models from Scratch
Fine-Tuning Large Language Models
Improving LLMs with RLHF

NOTE: Session on Saturdays or Sundays -- Please reach out to me via DM for other details.

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