About this Event
The offers the as part of its Information Technology and Programming Courses. This programme equips participants with essential skills in Python programming, data analysis, data visualisation, machine learning, and artificial intelligence. It covers practical techniques for processing data, developing predictive models, and applying AI solutions to real-world business and technical challenges.
Participants explore Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn to analyse datasets and create meaningful visualisations. The course also introduces supervised and unsupervised learning, model evaluation, neural networks, deep learning, and natural language processing. Through practical exercises, participants develop the ability to build, evaluate, and deploy machine learning models while understanding ethical considerations in AI and data science.
The Python for Data Science and AI Course supports professionals seeking to strengthen their technical expertise and apply data-driven methods across different industries. By combining programming fundamentals with advanced analytical concepts, the programme helps participants develop practical capabilities for solving complex problems, improving decision-making, and implementing intelligent solutions. Participants gain a valuable foundation for using Python in data science and AI applications, from initial data preparation to model development and deployment.
Objective:
The objectives of the Python for Data Science and AI Course are:
The aim of this course is to make attendees proficient in Python programming for data science and AI applications. Proficiently master Pandas, NumPy, Matplotlib, and Seaborn; build and evaluate ML models using real-world datasets; and comprehend AI principles, including inputting as well as implementing neural networks while diving into deep learning. Introduce Natural Language Processing (NLP) for text-based applications. Identify ethical considerations in AI and act accordingly. Develop a capstone project that displays the skills accrued throughout the course. Who Should Attend?
This course is ideal for:
Data science or AI career seekers and aspiring data scientists. Software engineers or developers wishing to adopt data science or AI.Decision-making using data insights by business analysts. Computer technology MLS-related professionals wishing to enrich themselves with machine learning and artificial intelligence. Students desirous of learning everything there is to know about data science and its potential uses in AI. How will attendees benefit?
After completing the course, attendees have the following benefits:
Hands-On Experience with Python: Practical applications of using Python in data science for all real-world applications of AI are also learnt by participants. This will include learning the operations of libraries such as Pandas, NumPy, Matplotlib, and Seaborn. Comprehensive Understanding of Data Science: They will solidify their skills in analysis, manipulation, and visualisation, which are very important in data science. He will learn how to clean, analyze, and present data properly. Proficiency in Machine Learning Models: In this course, attendees are enabled to make data-driven decisions regarding real-world applications through building, evaluating, and optimising models.Expertise in Artificial Intelligence and Deep Learning: Explains the basics of AI and deep learning. Building neural networks and implementing AI solutions based on TensorFlow and Keras frameworks will be done by the trainees. Hands-on Knowledge of Natural Language Processing (NLP): Attendees will perform practical applications of NLP techniques, sentiment analysis, text classification, and other machine learning applications involving text processing.Ethical Awareness in AI: This course will train attendees to understand the ethical issues with AI technologies and prepare them to apply AI in a responsible and fair way. This is pertinent in today's fast changing AI world. Solving Complex Problems with Confidence: The course will be able to train attendees with sufficient confidence in tackling complex problems through machine learning and AI techniques. It will enable the attendees to utilise Python for data analysis, model building, and decision-making. Networking and Collaboration: Through this programme, attendees will be able to interact with other professionals in the industries, other fellow learners, and instructors to connect them to a new source of career, new collaborations, or mentorship about data science and AI.
Attendees at the end of the course will not only gain valuable technical skills but also know how to apply Python for data science and AI in real-world projects.
Course Content
Introduction to Python Programming
- Basics of Python syntax and structures
- Working with Python libraries for data science
Data Manipulation with Pandas and NumPy
- Introduction to Pandas for dataframes
- Handling missing data, data cleaning, and transformations
- Working with NumPy arrays and mathematical operations
Data Visualization
- Using Matplotlib for creating basic plots
- Visualizing data with Seaborn for statistical graphics
- Advanced charting techniques for better data storytelling
Introduction to Machine Learning
- Overview of machine learning concepts
- Supervised learning algorithms (e.g., linear regression, decision trees)
- Unsupervised learning algorithms (e.g., clustering, dimensionality reduction)
Building and Evaluating Machine Learning Models
- Training and testing machine learning models
- Model evaluation using metrics like accuracy, precision, recall, and F1 score
- Hyperparameter tuning and cross-validation
Artificial Intelligence Foundations
- Introduction to AI concepts and applications
- Building neural networks using TensorFlow and Keras
- Deep learning techniques and frameworks
Natural Language Processing (NLP)
- Text processing and feature extraction
- Implementing basic NLP models for tasks like sentiment analysis and text classification
- Text generation and word embeddings (e.g., Word2Vec)
Ethical Considerations in AI and Data Science
- Understanding the ethical implications of AI technologies
- Responsible data use, privacy concerns, and fairness in AI models
Capstone Project
- Develop and deploy a machine learning or AI project to solve a real-world problem
- Present the project findings using data visualisation techniques
Event venue & nearby stays
British Academy, 133 Kilburn Lane, London, United Kingdom
Concerts, fests, parties, meetups — all the happenings, one place.