Introduction to AI, Data Science & Machine Learning with Python
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Introduction to AI, Data Science & Machine Learning with Python (DSC112)

Course Length: 5 days
Delivery Methods: Available as private class only
Course Overview

Welcome to the Introduction to AI, Data Science & Machine Learning with Python course, specially designed for companies aiming to enhance their teams' capabilities in navigating the modern data landscape. This course highlights the essential balance between technical expertise and non-technical insights required for data scientists, enabling participants to effectively transform data into actionable business strategies.

We begin by discovering the unique blend of skills a data scientist must possess, focusing on how technical proficiencies complement business acumen. You will explore the lifecycle of data science initiatives within an organization, learning to translate business inquiries into predictive models using AI and ML technologies. The lesson also demystifies the distinctions between generative and discriminative AI models, and the role of data scientists versus data engineers.

The next phase of the course provides practical experience in data manipulation and visualization, employing Python's powerful Pandas and Matplotlib libraries. Participants are guided through viewing datasets, handling diverse data forms, and employing techniques like filtering, grouping, and visualizing data for strategic communication purposes. Key lessons include managing data irregularities and presenting data insights effectively using Python's visualization tools.

Unstructured data, such as web content and emails, is often a goldmine for insights. This module will equip you with pre-processing skills and expose you to natural language processing (NLP) techniques, focusing on preparing data for analysis. You’ll engage with real-world applications of large language models to enhance data-driven decision-making processes.

Moreover, you’ll investigate how linear regression and feature engineering can be applied to solve business challenges. By expressing problems like customer revenue prediction through statistical models, you will learn to evaluate these models using Python, assess potential predictors, and leverage feature engineering to optimize model performance.

As predictive analytics becomes more integral to business strategy, understanding classification models is invaluable. Here, you will learn the process of building and using classifiers, such as decision trees, and evaluating their performance. This sets the stage for exploring alternative classification methods, including neural networks, logistic regression, and Naive Bayes classifiers, focusing on their probability underpinnings and performance measurement techniques like ROC curves and confusion matrices.

The course proceeds with clustering techniques to identify unique customer and product segments, highlighting the programmatic implementation of similarity concepts. Whether performing top-down clustering with k-means or bottom-up with hierarchical algorithms, you'll develop the skills to apply these techniques to structured and unstructured data.

Associative models and recommender systems offer a competitive edge in understanding customer behaviors. You'll learn to employ association rules to model these behaviors, strengthen these models using probability measures, and build personalized recommendation systems tailored to your business offerings.

A further module dives into network analysis, offering insights into organizational structures and relationships. By visualizing and analyzing these networks, employees can uncover unprecedented business insights for strategic decision-making.

The final phase tackles big data analytics and its ethical considerations. Exploring cloud solutions for managing large datasets, you will engage in the ethical discourse surrounding AI advancements and learn best practices in data communication. You'll also explore continuous learning opportunities, keeping your data science skills relevant in an ever-evolving field.

By course completion, you or your team will possess a robust introduction to key data science skills and methodologies, prepared to apply AI and ML models using Python to drive business growth and innovation. Your journey in data science will empower you to harness the transformative power of data for your organization's success.

Course Benefits
  • Gain a comprehensive understanding of the role and skillset required for a successful Data Scientist.
  • Develop proficiency in using Python's Pandas, Matplotlib, and Seaborn libraries for data manipulation and visualization.
  • Learn how to preprocess and analyze unstructured data using Natural Language Processing (NLP) techniques.
  • Master the application of linear regression and feature engineering to solve business problems.
  • Understand the construction and evaluation of classification models for predictive analysis.
  • Explore alternative approaches to classification, including neural networks and deep learning.
  • Discover clustering techniques for customer and product segmentation.
  • Build recommender systems and evaluate them using association rules.
  • Analyze organizational networks to uncover insights and drive business strategies.
  • Learn the ethical implications of big data analytics and AI, and understand the importance of communication in data science.
Course Outline
  1. The Role of a Data Scientist: Combining Technical and Non-Technical Skills
    1. What is the required skillset of a Data Scientist?
    2. Combining the technical and non-technical roles of a Data Scientist
    3. The difference between a Data Scientist and a Data Engineer
    4. Exploring the entire lifecycle of Data Science efforts within the organization
    5. Turning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models
    6. Exploring diverse and wide-ranging data sources that you can use to answer business questions
    7. Examine the difference between Generative AI and Discriminative AI
  2. Data Manipulation and Visualization using Python's Pandas and Matplotlib Libraries
    1. Introducing the features of Python that are relevant to Data Scientists and Data Engineers
    2. Viewing Data Sets using Python’s Pandas library
    3. Importing, exporting, and working with all forms of data, from Relational Databases to Google Images
    4. Using Python Selecting, Filtering, Combining, Grouping, and Applying Functions from Python's Pandas library
    5. Dealing with Duplicates, Missing Values, Rescaling, Standardizing, and Normalizing Data
    6. Visualizing data for both exploration and communication with the Pandas, Matplotlib, and Seaborn Python libraries
  3. Preprocessing and Analyzing Unstructured Data with Natural Language Processing
    1. Preprocessing Unstructured Data such as web adverts, emails, and blog posts for AI/ML models
    2. Exploring the most popular approaches to Natural Language Processing (NLP), such as stemming and "stop" words
    3. Preparing a term-document matrix (TDM) of unstructured documents for analysis
    4. Look at how Data Scientists can integrate Large Language Models (LLMs) in their work
  4. Linear Regression and Feature Engineering for Business Problem Solving
    1. Expressing a business problem, such as customer revenue prediction, as a linear regression task
    2. Assessing variables as potential Predictors of the required Target (e.g., Education as a predictor of Salary Build)
    3. Interpreting and Evaluating a Linear Regression model in Python using measures such as RMSE
    4. Exploring the Feature Engineering possibilities to improve the Linear Regression model
  5. Classification Models and Evaluation for Predictive Analysis
    1. Learning how AI/ML Classifiers are built and used to make predictions such as Customer Churn
    2. Exploring how AI/ML Classification models are built using Training, Test, and Validation
    3. Evaluating the strength of a Decision Tree Classifier
  6. Alternative Approaches to Classification and Model Evaluation
    1. Examining alternative approaches to classification
    2. Considering how Activation Functions are integral to Logistic Regression Classifiers
    3. Investigating how Neural Networks and Deep Learning are used to build self-driving cars
    4. Exploring the probability foundations of Naive Bayes classifiers
    5. Reviewing different approaches to measuring the performance of AI/ML Classification Models
    6. Reviewing ROC curves, AUC measures, Precision, Recall, and Confusion Matrices
  7. Clustering Techniques for Customer and Product Segmentation
    1. Uncovering new ways of segmenting your customers, products, or services using clustering algorithms
    2. Exploring what the concept of similarity means to humans and how you can implement it programmatically through distance measures on descriptive variables
    3. Performing top-down clustering with Python’s Scikit-Learn K-Means algorithm
    4. Performing bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm
    5. Examining clustering techniques on unstructured data (e.g., Tweets, Emails, Documents, etc.)
  8. Association Rules and Recommender Systems for Business Applications
    1. Building models of customer behaviors or business events from logged data using Association Rules
    2. Evaluating the strength of these models through probability measures of support, confidence, and lift
    3. Employing feature engineering approaches to improve the models
    4. Building a recommender for your customers that is unique to your product/service offering
  9. Network Analysis for Organizational Insights
    1. Analyzing your organization, its people, and its environment as a network of inter-relationships
    2. Visualizing these relationships to uncover previously unseen business insights
    3. Exploring ego-centric and socio-centric methods of analyzing connections critical to your organization
  10. Big Data Analytics, Communication, and Ethics
    1. Examining Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics
    2. Exploring the communications and ethics aspects of being a Data Scientist
    3. Discuss the ethical implications of recent developments in AI
    4. Surveying the paths of continual learning for a Data Scientist
Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

Live Private Class
  • Private Class for your Team
  • Live training
  • Online or On-location
  • Customizable
  • Expert Instructors
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