Machine Learning Essentials with Python (TTML5506-P)

Overview

Dive into the fascinating world of AI and Machine Learning with our three-day, comprehensive course, “Machine Learning Essentials with Python”. This course, perfect for basic Python developers, equips you with the skills to leverage Python for intelligent applications like data analysis, predictive modeling, automation, and chatbots, transforming your project capabilities. Participants will get hands-on experience with popular machine learning algorithms, exploring their potential applications and limitations.

Our highly-experienced instructors will share their practical expertise, guiding you through learning these new skills and empowering you to confidently apply them in your job or role. Throughout the course you’ll explore learning and using Supervised and Unsupervised Learning techniques, Data Wrangling and Preprocessing, Ensemble Learning, and Model Evaluation and Validation. Hands-on labs replicating real-world scenarios form a core part of the learning experience, ensuring you acquire practical, applicable skills. Each hands-on lab will provide you with practical experience using innovative skills with cutting edge tools, applied in a practical and meaningful way.

Audience Profile

This course is ideally suited for Python developers, data analysts, and aspiring data scientists looking to expand their skills into AI and Machine Learning. It is also highly beneficial for product managers and business leaders aiming to acquire a hands-on understanding of AI’s impact on product development and business strategy.

At Course Completion

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll learn how to:

· Master the Python Programming for Data Science: Gain an in-depth understanding of Python’s role in data science and AI, including proficiency in using key Python data science libraries like Pandas, NumPy, and Matplotlib.

· Understand the Fundamentals of AI and Machine Learning: Develop a strong grasp of AI and Machine Learning concepts, their applications, and how to differentiate between AI, Machine Learning, and Deep Learning.

· Dive into Supervised and Unsupervised Learning Techniques: Acquire hands-on skills to conduct Regression Analysis, Binary Classification, and k-means Clustering – key methods in Supervised and Unsupervised Learning.

· Apply Data Wrangling and Preprocessing Techniques: Learn to handle missing data, outliers, and categorical data effectively and perform feature scaling and normalization – crucial steps in Machine Learning projects.

· Create and Evaluate Machine Learning Models: Get a grip on the lifecycle of AI projects, including model creation, evaluation, validation, and the application of Ensemble Learning techniques.

· Understand and implement crucial data preprocessing techniques in Python: Attendees will acquire the ability to handle missing data, outliers, and categorical data, essential for creating reliable machine learning models.

· Develop competency in creating and interpreting data visualizations: Students will learn how to leverage Python’s powerful libraries such as Matplotlib and Seaborn to create compelling visualizations and extract meaningful insights from data.

· Construct a machine learning pipeline for real-world applications: Participants will gain the practical know-how to carry a machine learning project from initial data collection through to final model deployment, using Python.

· (Optional / Bonus Topics): Implement AI into Real-World Applications: By the end of the course, you’ll be able to build applications that integrate AI functionalities, using popular Python frameworks and modern AI technologies, like GPT-4.