Introduction to AI, Data Science, and Machine Learning with Python

In the rapidly evolving world of technology, Artificial Intelligence (AI), Data Science, and Machine Learning (ML) have emerged as pivotal disciplines that drive innovation and strategic decision-making across industries. Companies are increasingly harnessing the power of data to gain insights, improve efficiency, and maintain a competitive edge. Consequently, there is a growing demand for professionals equipped with the skills to navigate and leverage these advanced technologies.

Our comprehensive course, “Introduction to AI, Data Science, and Machine Learning with Python,” is designed to equip you with the foundational skills and techniques necessary to excel in these dynamic fields. This course offers a deep dive into the world of data science, starting from the basics and progressing to advanced concepts and applications.

Duration: 5 Days
Level: Foundation
Prerequisite: None

Course Overview

You will begin your journey by understanding the critical role of a data scientist and the lifecycle of data science initiatives within an organisation. This foundational knowledge will provide context for the technical skills and tools you will master throughout the course.

Key Learning Outcomes

Understanding Data Science and the Role of a Data Scientist
Gain insights into the responsibilities and impact of a data scientist in various industries.
Learn about the data science lifecycle, including data collection, preparation, analysis, modelling, and deployment.

Technical Skills with Python
Develop proficiency in Python, a versatile programming language widely used in data science.
Explore essential Python libraries such as Pandas for data manipulation, NumPy for numerical operations, and Matplotlib and Seaborn for data visualisation.

Data Preprocessing and Analysis
Learn techniques for cleaning and preprocessing unstructured data to make it suitable for analysis.
Conduct exploratory data analysis (EDA) to uncover patterns, correlations, and insights.
Building AI and Machine Learning Models
Understand the principles of AI and machine learning and how they differ from traditional programming.
Dive into key machine learning algorithms, including:
Linear Regression: Predict continuous outcomes based on input features.
Decision Tree Classifiers: Build models that split data into meaningful categories.
Clustering Algorithms: Group data points into clusters based on similarity.

Real-World Applications
Apply machine learning techniques to solve practical problems, such as:
Predicting Customer Churn: Identify customers likely to leave and take preventive actions.
Building Recommendation Engines: Develop systems that suggest products or content based on user preferences.
Hands-On Projects and Portfolio Development
Engage in hands-on exercises and projects designed to reinforce your learning and provide practical experience.
Build a portfolio of work that demonstrates your skills and knowledge to potential employers.

Course Structure

This course is structured to ensure a balanced blend of theory and practical application. You will participate in interactive lectures, engage with real-world datasets, and complete hands-on projects that simulate industry scenarios.

Course Overview
“Introduction to AI, Data Science, and Machine Learning with Python” is an intensive five-day course designed for corporate customers. This course comprehensively explores AI, data science, and machine learning principles, focusing on practical applications and hands-on experience with Python. The course is structured into ten modules, each providing participants with a deep understanding of key concepts and techniques, enabling them to apply these skills to real-world business problems.

Module 1: The Role of a Data Scientist: Combining Technical and Non-Technical Skills
What is the required skillset of a Data Scientist?
Combining the technical and non-technical roles of a Data Scientist
The difference between a Data Scientist and a Data Engineer
Exploring the entire lifecycle of Data Science efforts within the organisation
Turning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models
Exploring diverse and wide-ranging data sources that you can use to answer business questions
Examine the difference between Generative AI and Discriminative AI

Module 2: Data Manipulation and Visualization using Python’s Pandas and Matplotlib Libraries

Introducing the features of Python that are relevant to Data Scientists and Data Engineers
Viewing Data Sets using Python’s Pandas library
Importing, exporting, and working with all forms of data, from Relational Databases to Google Images
Using Python Selecting, Filtering, Combining, Grouping, and Applying Functions from Python’s Pandas library
Dealing with Duplicates, Missing Values, Rescaling, Standardizing, and Normalizing Data
Visualising data for both exploration and communication with the Pandas, Matplotlib, and Seaborn Python libraries

Module 3: Preprocessing and Analysing Unstructured Data with Natural Language Processing
Preprocessing Unstructured Data such as web adverts, emails, and blog posts for AI/ML models
Exploring the most popular approaches to Natural Language Processing (NLP), such as stemming and “stop” words
Preparing a term-document matrix (TDM) of unstructured documents for analysis
Look at how Data Scientists can integrate Large Language Models (LLMs) in their work

 

Module 4: Linear Regression and Feature Engineering for Business Problem Solving

Expressing a business problem, such as customer revenue prediction, as a linear regression task
Assessing variables as potential Predictors of the required Target (e.g., Education as a predictor of Salary Build)
Interpreting and Evaluating a Linear Regression model in Python using measures such as RMSE
Exploring the Feature Engineering possibilities to improve the Linear Regression model

Module 5: Classification Models and Evaluation for Predictive Analysis

Learning how AI/ML Classifiers are built and used to make predictions such as Customer Churn
Exploring how AI/ML Classification models are built using Training, Test, and Validation
Evaluating the strength of a Decision Tree Classifier

Module 6: Alternative Approaches to Classification and Model Evaluation

Examining alternative approaches to classification
Considering how Activation Functions are integral to Logistic Regression Classifiers
Investigating how Neural Networks and Deep Learning are used to build self-driving cars
Exploring the probability foundations of Naive Bayes classifiers
Reviewing different approaches to measuring the performance of AI/ML Classification Models
Reviewing ROC curves, AUC measures, Precision, Recall, and Confusion Matrices

Module 7: Clustering Techniques for Customer and Product Segmentation

Uncovering new ways of segmenting your customers, products, or services using clustering algorithms
Exploring what the concept of similarity means to humans and how you can implement it programmatically through distance measures on descriptive variables
Performing top-down clustering with Python’s Scikit-Learn K-Means algorithm
Performing bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm
Examining clustering techniques on unstructured data (e.g., Tweets, Emails, Documents, etc.)

Module 8: Association Rules and Recommender Systems for Business Applications

Building models of customer behaviours or business events from logged data using Association Rules
Evaluating the strength of these models through probability measures of support, confidence, and lift
Employing feature engineering approaches to improve the models
Building a recommender for your customers that is unique to your product/service offering

Module 9: Network Analysis for Organizational Insights

Analysing your organisation, its people, and its environment as a network of inter-relationships
Visualising these relationships to uncover previously unseen business insights
Exploring ego-centric and socio-centric methods of analysing connections critical to your organisation

Module 10: Big Data Analytics, Communication, and Ethics

Examining Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics
Exploring the communications and ethics aspects of being a Data Scientist
Discussing the ethical implications of recent developments in AI
Surveying the paths of continual learning for a Data Scientist

Conclusion

By the end of this course, you will have a robust understanding of the data science process, the tools and techniques used by data scientists, and the ability to apply these skills to real-world problems. Whether you want to start a career in data science or enhance your existing skill set, “Introduction to AI, Data Science, and Machine Learning with Python” will provide you with the knowledge and experience needed to succeed in this exciting field.

This five-day intensive course provides a comprehensive foundation in AI, data science, and machine learning using Python. Participants will gain practical experience through hands-on exercises and projects, enabling them to apply these techniques to real-world business problems. By the end of the course, participants will be well-equipped to drive data-driven decision-making and innovation within their organisations.

Join us at CTTI and embark on a journey to become a proficient data scientist, ready to tackle the challenges of tomorrow with confidence and expertise.