Complete Data Science, ML, DL & NLP Bootcamp

🧠 Complete Data Science, ML, DL & NLP Bootcamp

Duration: 4–6 months | Mode: Offline | Level: Beginner to Advanced
Certificate: Yes | Live Projects: Included

Β 

πŸ“ Course Overview

Β 

ETU Academy’s Bootcamp is designed to transform beginners into skilled data scientists. Covering core data handling, machine learning, deep learning, and NLP, this course combines theory with real-world applications and hands-on projects.


Β 

πŸ“˜ Syllabus Breakdown

Β 

πŸ”Ή Module 1: Python for Data Science

  • Python basics: variables, loops, functions

  • NumPy, Pandas for data manipulation

  • Data visualization: Matplotlib, Seaborn

  • Exploratory Data Analysis (EDA)

πŸ”Ή Module 2: Statistics & Probability

  • Descriptive & inferential statistics

  • Probability distributions

  • Hypothesis testing

  • Correlation & regression

πŸ”Ή Module 3: Data Wrangling & Cleaning

  • Handling missing data

  • Data type conversions

  • Feature engineering

  • Data pipelines

πŸ”Ή Module 4: Machine Learning

  • Supervised vs. Unsupervised learning

  • Regression & classification algorithms

  • Decision Trees, Random Forest, KNN

  • Clustering: K-Means, DBSCAN

  • Model evaluation & tuning (cross-validation, GridSearchCV)

πŸ”Ή Module 5: Deep Learning (DL)

  • Neural networks basics

  • Activation functions & optimizers

  • TensorFlow & Keras introduction

  • CNNs for image classification

  • RNNs and LSTM for sequences

πŸ”Ή Module 6: Natural Language Processing (NLP)

  • Text preprocessing (Tokenization, Lemmatization)

  • Sentiment analysis

  • Word embeddings: Word2Vec, GloVe

  • Transformers & BERT (introductory level)

  • Chatbot & language model mini-projects

πŸ”Ή Module 7: Model Deployment

  • Streamlit & Flask for app building

  • Saving models using Pickle/joblib

  • Deploying models on cloud (Heroku, Render)

πŸ”Ή Module 8: Capstone Projects

  • Real-world datasets & client scenarios

  • Group projects & presentations

  • Git/GitHub version control

  • Peer code reviews


🎯 Key Highlights

  • πŸ“Š Hands-on with tools: Jupyter, Scikit-learn, TensorFlow, Keras, NLTK, HuggingFace

  • πŸ“ Real datasets from Kaggle, UCI, and internal case studies

  • πŸ§‘β€πŸ« 1-on-1 mentorship and career guidance

  • πŸ’Ό Resume and LinkedIn profile building sessions

Scroll to Top