Training on Introduction to AI, Data Science & Machine Learning with Python

This comprehensive course is designed to equip professionals with the foundational skills and techniques needed to succeed in the rapidly growing field of data science. Participants will learn the data science lifecycle, from data analysis and visualization using Python and its libraries to preprocessing unstructured data and building AI/ML models. The training provides a strategic framework for applying these techniques to solve real-world problems.

Duration

5 Days

Who Should Attend:

• Aspiring data scientists and machine learning engineers

• Data analysts and business intelligence professionals

• Managers and team leaders seeking to understand data-driven strategies

• Professionals in marketing, finance, and operations

• Anyone interested in a career in AI, data science, and analytics

Organisational Impact

• Strengthens organisational capacity to leverage data-driven insights for strategic decision-making.

• Enhances competitiveness by equipping teams with skills in AI, data science, and machine learning applications.

• Reduces dependency on external consultants by building in-house expertise for data analysis and predictive modeling.

• Improves operational efficiency through automation and intelligent systems powered by machine learning.

• Supports innovation in products, services, and customer engagement through data-driven strategies.

Personal Impact

• Equips participants with foundational skills in Python for data science, AI, and machine learning.

• Builds confidence in applying key algorithms such as regression, classification, and clustering to solve problems.

• Provides hands-on experience in real-world applications like customer churn prediction and recommendation systems.

• Expands career opportunities in the rapidly growing fields of AI, data science, and analytics.

• Empowers learners to build a strong portfolio of projects showcasing applied skills in AI and ML.

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

• Differentiate between Predictive AI and Generative AI.
• Translate everyday business questions and problems into Machine Learning tasks to make data-driven decisions.
• Use Python Pandas, Matplotlib & Seaborn libraries to explore, analyze, and visualize data from various sources, including the web, word documents, email, NoSQL stores, databases, and data warehouses.
• Train a Machine Learning Classifier using different algorithmic techniques from the Scikit-Learn library, such as Decision Trees, Logistic Regression, and Neural Networks.
• Re-segment your customer market using K-Means and Hierarchical algorithms to better align products and services to customer needs.
• Discover hidden customer behaviors from Association Rules and build a Recommendation Engine based on behavioral patterns.
• Investigate relationships & flows between people and business-relevant entities using Social Network Analysis.
• Build predictive models of revenue and other numeric variables using Linear Regression.
• Leverage continued support with after-course one-on-one instructor coaching and computing sandbox.

Module 1: The Strategic Role of a Data Scientist

• Required technical and non-technical skillsets
• Distinction between Data Scientist and Data Engineer
• Full lifecycle of data science initiatives in an organization
• Translating business questions into AI and ML models
• Understanding data sources for analytical insights
• Difference between Generative AI and Discriminative AI

Module 2: Data Manipulation and Visualization with Python

• Introduction to Python for data science and engineering
• Data import, export, and handling from diverse sources
• Using Pandas for selecting, filtering, grouping, and applying functions
• Managing duplicates, missing values, normalization, and scaling
• Visual analytics using Pandas, Matplotlib, and Seaborn

Module 3: Natural Language Processing and Unstructured Data Analysis

• Preprocessing web content, emails, and free-text data
• Techniques such as stemming and removal of stop words
• Building a term-document matrix (TDM)
• Integrating Large Language Models (LLMs) in data analysis

Module 4: AI Ethics, Big Data Analytics and Professional Communication

• Cloud-based analytics (Microsoft Azure, AWS, Google Cloud)
• Ethical implications of AI developments
• Communication responsibilities of a data scientist
• Career development and continuous learning in the field

Module 5: Machine Learning Evaluation, Classification & Clustering Techniques

• Overview of classification methods (e.g., logistic regression, neural networks)
• Activation functions and their role in model development
• Probability foundations of Naive Bayes classifiers
• Model performance measures (ROC, AUC, precision, recall, confusion matrix)
• Customer and product segmentation using clustering algorithms
• K-Means and hierarchical clustering with Scikit-Learn
• Clustering applications on unstructured data (tweets, emails, documents)