The British Academy for Training and Development offers a comprehensive course of Python for Data Science and AI, designed to train attendees with the essential skills and knowledge for working with data science and artificial intelligence using Python. This course takes you through how to use Python programming for data analysis and helps you with various data manipulation libraries. It also includes the usage of Matplotlib and Seaborn for data visualisation, thus creating insightful graphs and charts for the user. The course would also discuss various machine learning algorithms that include supervised learning, unsupervised learning, and model evaluation. Equally, there are such concepts in AI as model building and deployment using Python frameworks. It is learned as hands-on experience in neural networks, deep learning, and natural language processing (NLP). The course will also offer you the understanding of what ethical issues there are in AI and data science applications.

In this course you will be sure to master the use of Python in applying science models to real-life applications, analysis, and visualisation of data as well as build and deploy Machine Learning models for real-world implementation.

Objective:

The objective of 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 on inputting as well as implementing neural networks While diving into deep learning. Introducing Natural Language Processing (NLP) for text-based applications Identify ethical considerations on 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 are 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 professions 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 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 his 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 basic AIs 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, models 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.

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 visualization techniques