Python for Data Science and AI

Course Info

Length: 1 Week

City: London

Type: In Classroom

Available Dates

  • Jan-20-2025

    London

  • Mar-31-2025

    London

  • May-19-2025

    London

  • July-21-2025

    London

  • Sep-29-2025

    London

  • Nov-17-2025

    London

Dates in Other Venues

  • Feb-17-2025

    Dubai

  • Mar-31-2025

    Istanbul

  • Mar-31-2025

    Kuala Lumpur

  • Mar-31-2025

    Barcelona

  • Mar-31-2025

    Singapore

  • Mar-31-2025

    Paris

  • Mar-31-2025

    Amsterdam

  • Apr-21-2025

    Dubai

  • June-30-2025

    Kuala Lumpur

  • June-30-2025

    Istanbul

  • June-30-2025

    Barcelona

  • June-30-2025

    Singapore

  • June-30-2025

    Dubai

  • June-30-2025

    Amsterdam

  • June-30-2025

    Paris

  • Aug-18-2025

    Dubai

  • Sep-29-2025

    Paris

  • Sep-29-2025

    Amsterdam

  • Sep-29-2025

    Barcelona

  • Sep-29-2025

    Singapore

  • Sep-29-2025

    Istanbul

  • Sep-29-2025

    Kuala Lumpur

  • Oct-20-2025

    Dubai

  • Dec-29-2025

    Barcelona

  • Dec-29-2025

    Dubai

  • Dec-29-2025

    Istanbul

  • Dec-29-2025

    Singapore

  • Dec-29-2025

    Paris

  • Dec-29-2025

    Kuala Lumpur

  • Dec-29-2025

    Amsterdam

Course Details

Course Outline

5 days course

  • Complete Python Basics and Hands-on exercises.
  • oops concepts in python
  • Exploring Python data structures (lists, tuples, dictionaries, sets).
  • Practicals on classes and objects in Python.
  • Comprehensive exploration of EDA using Matplotlib and Seaborn.
  • Understanding and handling data issues: imbalance, skewness, correlation, outliers, null values etc
  • Univariate and bivariate analysis techniques.
  • Data preprocessing and manipulation using Pandas.
  • Practical exercises to find insights from EDA and Pandas.
  • Machine Learning with Python
  • Introduction to machine learning concepts: supervised, unsupervised learning, classification, regression.
  • Hands-on with various ML algorithms: Linear Regression, Logistic Regression, KNN, SVM.
  • Exploring Boosting algorithms: Gradient Boosting, XGBoost.
  • Real-time machine learning project implementation.
  • Deep Learning Fundamentals
  • Basics of neural networks and their architectures.
  • Detailed exploration of ANN, CNN, RNN, LSTM.
  • Practical session on a real-time deep learning project.
  • Introduction to frameworks like TensorFlow and Keras,pytorch.
  • Natural Language Processing and Generative AI
  • Introduction to NLP and its applications.
  • Working with NLP libraries: NLTK, Spacy, OCR tools.
  • Projects on sentiment analysis and topic modeling.
  • Introduction to Generative AI concepts.
  • Building a domain-specific chatbot using the ChatGPT API.

Course Video