Job Role

Data Science

Master data analysis, machine learning, and AI to unlock insights from complex datasets.

Course Syllabus

1

Introduction to Data Science

  • Basic Foundations
2

Python Programming

  • Fundamentals of Python programming language
  • Advanced Python concepts (decorators, generators, context managers)
  • Python libraries for data science (NumPy, Pandas, Matplotlib, Seaborn)
3

Data Analysis and Visualization

  • Data cleaning and preprocessing techniques
  • Exploratory Data Analysis (EDA) with Pandas and visualization libraries
  • Statistical analysis and hypothesis testing
4

SQL for Data Science

  • SQL fundamentals (queries, joins, aggregations)
  • Working with relational databases (SQLite, MySQL)
  • Data manipulation and querying for analysis tasks
5

Machine Learning

  • Supervised learning algorithms (linear regression, logistic regression, decision trees, SVMs)
  • Model evaluation and selection techniques
  • Hyperparameter tuning and cross-validation
  • Introduction to ensemble methods and boosting algorithms
6

Deep Learning

  • Neural network basics (perceptrons, activation functions)
  • Building deep neural networks with TensorFlow or PyTorch
  • Convolutional Neural Networks (CNNs) for computer vision tasks
  • Recurrent Neural Networks (RNNs) for sequential data and NLP tasks
7

Computer Vision

  • Image preprocessing and augmentation techniques
  • Object detection algorithms (YOLO, SSD, Faster R-CNN)
  • Transfer learning for computer vision tasks
  • Evaluation metrics for object detection models
8

Natural Language Processing (NLP)

  • Text preprocessing techniques (tokenization, stemming, lemmatization)
  • Word embeddings (Word2Vec, GloVe)
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for NLP tasks
  • Transformer-based models for NLP (BERT, GPT)
9

Statistics

  • Probability distributions and hypothesis testing
  • Descriptive and inferential statistics
  • Bayesian statistics for machine learning
10

Model Deployment on Hugging Face

  • Introduction to Hugging Face Transformers library
  • Deploying machine learning models on the Hugging Face Model Hub
  • Fine-tuning pre-trained models for specific tasks
  • Hosting and serving models using Hugging Face's infrastructure
11

Generative AI

  • Google Gemini, Gemini Pro Vision
  • Large language models
  • OpenAI's ChatGPT
  • Generative Pre-Trained Transformers
  • META's LLAMA, LLAMA 2
  • Creating LLM's from scratch and Fine-tune
  • Prompt Engineering
  • LORA & CLORA
12

Capstone Project

  • Emphasis on end-to-end 25+ project development, from data collection to model deployment.

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