Course Overview

Python has become the leading programming language for Data Science, Machine Learning, Artificial Intelligence, and advanced analytics. Its simplicity, extensive libraries, and powerful data-processing capabilities make it an essential skill for professionals seeking to extract insights from data and develop intelligent solutions. As organisations increasingly adopt AI-driven technologies, the demand for Python expertise continues to grow across industries.

The Python for Data Science and Artificial Intelligence Programme by Transformentors Academy is an intensive five-day course designed to provide participants with practical programming skills and a strong foundation in data science, machine learning, deep learning, and natural language processing (NLP). The programme combines fundamental programming concepts with hands-on applications that demonstrate how Python can be used to solve real-world business and analytical challenges.

Participants will learn how to work with data, perform analysis, build predictive models, and develop AI-powered solutions using widely adopted Python libraries and tools. Through practical exercises, case studies, and guided projects, learners will gain experience in transforming raw data into actionable insights and implementing machine learning and AI techniques.

By the end of the programme, participants will have the knowledge and practical experience required to apply Python in data science and artificial intelligence projects, supporting data-driven decision-making and innovation within their organisations.

Agenda

Day — 1 Python Programming Fundamentals

• Understanding Python fundamentals, syntax, and programming concepts.
• Working with variables, data types, operators, and control structures.
• Applying Object-Oriented Programming (OOP) concepts in Python.
• Understanding classes, objects, inheritance, encapsulation, and polymorphism.
• Exploring Python data structures, including lists, tuples, dictionaries, and sets.
• Performing data manipulation and processing using Python collections.
• Hands-On Exercises: Building Python programs using core programming concepts.
• Practical Lab: Creating and working with classes and objects in Python.

Day — 2 Exploratory Data Analysis (EDA) and Data Preprocessing

• Performing Exploratory Data Analysis (EDA) using Matplotlib and Seaborn.
• Understanding data distributions and identifying meaningful patterns.
• Handling common data quality issues such as imbalance, skewness, outliers, missing values, and correlation.
• Applying univariate and bivariate analysis techniques for data exploration.
• Using Pandas for data cleaning, preprocessing, and manipulation.
• Transforming and preparing datasets for machine learning applications.
• Creating visualisations to support data-driven insights and decision-making.
• Practical Exercises: Analysing datasets and extracting insights using EDA techniques and Pandas.

Day — 3 Machine Learning with Python

• Understanding machine learning concepts, including supervised and unsupervised learning.
• Exploring classification and regression techniques for predictive modelling.
• Implementing machine learning algorithms such as Linear Regression, Logistic Regression, K-Nearest Neighbours (KNN), and Support Vector Machines (SVM).
• Understanding model training, testing, and evaluation techniques.
• Exploring ensemble learning methods, including Gradient Boosting and XGBoost.
• Applying machine learning techniques to solve real-world business problems.
• Hands-On Lab: Building and evaluating machine learning models using Python.
• Project: Developing an end-to-end machine learning solution using real-world datasets.

Day — 4 Deep Learning Fundamentals

• Understanding the fundamentals of neural networks and deep learning.
• Exploring deep learning architectures, including ANN, CNN, RNN, and LSTM.
• Understanding the applications of deep learning in computer vision, prediction, and sequence modelling.
• Building and training neural network models using real-world datasets.
• Introduction to popular deep learning frameworks such as TensorFlow, Keras, and PyTorch.
• Hands-On Lab: Developing and evaluating a deep learning project.

Day — 5 Natural Language Processing and Generative AI

• Introduction to Natural Language Processing (NLP) and its business applications.
• Working with NLP libraries such as NLTK and SpaCy.
• Exploring OCR tools for text extraction and document processing.
• Performing text preprocessing, analysis, and feature extraction.
• Hands-On Projects: Sentiment analysis and topic modelling.
• Understanding the fundamentals of Generative AI and Large Language Models (LLMs).
• Exploring practical applications of Generative AI in business and automation.
• Building a domain-specific chatbot using the ChatGPT API.
• Final Project: Developing an NLP or Generative AI solution using real-world data.

Learning Outcomes

By the end of this programme, participants will be able to:

• Master Python programming fundamentals, including data structures and object-oriented programming concepts.
• Develop Python applications using industry-standard coding practices and techniques.
• Perform Exploratory Data Analysis (EDA) using Python libraries such as Pandas, Matplotlib, and Seaborn.
• Apply data cleaning, preprocessing, and transformation techniques to prepare datasets for analysis.
• Understand and implement key machine learning algorithms for predictive analytics and classification tasks.
• Explore advanced machine learning techniques, including ensemble methods such as Gradient Boosting and XGBoost.
• Understand deep learning concepts and neural network fundamentals.
• Apply deep learning architectures, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) models.
• Explore Natural Language Processing (NLP) concepts and applications using libraries such as NLTK and SpaCy.
• Develop practical NLP solutions, including sentiment analysis, text processing, and chatbot applications.

Who Should Attend

This programme is designed for individuals seeking to develop practical Python skills for data science, machine learning, and artificial intelligence, including:

• Data Analysts and aspiring Data Scientists.
• Professionals looking to apply Python-based data analysis and machine learning in their roles.
• Business Intelligence and Analytics Professionals.
• Software Developers and Technical Professionals interested in AI applications.
• Researchers and Academic Professionals working with data-driven projects.
• Students seeking to build a career in Data Science, Machine Learning, or Artificial Intelligence.
• Anyone interested in developing practical Python skills for data analytics and AI solutions.