{"product_id":"introduction-to-ai-data-science-machine-learning-with-python-1","title":"Introduction to AI, Data Science \u0026 Machine Learning with Python","description":"\u003cdiv\u003e\n\u003cp\u003eData is at the heart of modern business decision-making, but in today's AI-driven world, Data Scientists need more than traditional analytics skills alone. Organizations are increasingly looking for professionals who can combine Data Science, Machine Learning, Python, and Generative AI to solve real business problems and deliver measurable value.\u003c\/p\u003e\r\n\u003cp\u003eIn this hands-on course, you'll learn the complete Data Science lifecycle, from translating business questions into analytical problems, to exploring data, building predictive models, communicating insights, and leveraging modern AI tools. Along the way, you'll discover how Generative AI is transforming the role of the Data Scientist and learn practical ways to use AI assistants to accelerate coding, analysis, visualization, and model interpretation.\u003c\/p\u003e\r\n\u003cp\u003eUsing Python and industry-standard libraries such as Pandas, Matplotlib, Seaborn, and Scikit-Learn, you'll build real-world solutions including customer churn models, recommendation systems, customer segmentation models, predictive forecasting models, and social network analyses.\u003c\/p\u003e\r\n\u003cp\u003eYou'll also explore emerging topics shaping the future of the profession, including Foundation Models, GPTs, Retrieval-Augmented Generation (RAG), embeddings, AI agents, synthetic data, Explainable AI, and Responsible AI.\u003c\/p\u003e\r\n\u003cp\u003eThrough practical exercises, guided labs, and AI-assisted challenges, you'll gain hands-on experience applying Data Science and Machine Learning techniques to realistic business scenarios. By the end of the course, you'll understand not only how Data Science is practiced today, but how it is evolving in the Age of AI.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eIntroduction to AI, Data Science \u0026amp; Machine Learning with Python Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand the role of the modern Data Scientist and how Machine Learning, Generative AI, and AI-assisted workflows fit into the Data Science lifecycle\u003c\/li\u003e\n\u003cli\u003eTranslate business questions into Machine Learning and AI solutions that support data-driven decision-making\u003c\/li\u003e\n\u003cli\u003eUse Python, Pandas, and AI assistants to acquire, explore, analyze, and visualize data\u003c\/li\u003e\n\u003cli\u003eLearn how Generative AI can accelerate coding, data preparation, visualization, reporting, model interpretation, and analytical workflows\u003c\/li\u003e\n\u003cli\u003eApply Exploratory Data Analysis (EDA) techniques to uncover patterns, assess data quality, detect bias, and evaluate model readiness\u003c\/li\u003e\n\u003cli\u003eExplore contemporary AI concepts including Foundation Models, GPTs, embeddings, Retrieval-Augmented Generation (RAG), synthetic data, and AI agents\u003c\/li\u003e\n\u003cli\u003eBuild predictive models using Linear Regression, Logistic Regression, Decision Trees, Naïve Bayes, and Neural Networks, while learning how Generative AI can assist model development and interpretation\u003c\/li\u003e\n\u003cli\u003eSegment customers using Clustering, discover purchasing patterns using Association Rules, and build Recommendation Systems that support personalization and business growth\u003c\/li\u003e\n\u003cli\u003eAnalyze relationships between people, products, and organizations using Social Network Analysis, graph analytics, and modern AI applications\u003c\/li\u003e\n\u003cli\u003eUnderstand the importance of Responsible AI, Explainable AI, fairness, governance, and the future of Data Science in the Age of AI\u003c\/li\u003e\n\u003cli\u003eTest your knowledge with an end-of-course assessment\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTraining Prerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eNone.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eData Science Training in Python Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: The Modern Data Scientist and AI Landscape\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExplore how the role of the Data Scientist is evolving in the age of Generative AI, Foundation Models, and AI-assisted analytics\u003c\/li\u003e\n\u003cli\u003eUnderstand the technical, analytical, business, and communication skills required of modern Data Scientists\u003c\/li\u003e\n\u003cli\u003eExamine how Data Scientists, Data Engineers, Machine Learning Engineers, and AI Engineers differ in their roles of delivering value from data\u003c\/li\u003e\n\u003cli\u003eFollow the complete Data Science lifecycle, from business problem definition and data acquisition through model development, deployment, and governance\u003c\/li\u003e\n\u003cli\u003eLearn how to translate business questions into Data Science, Machine Learning, and AI opportunities\u003c\/li\u003e\n\u003cli\u003eExplore the concepts behind Foundation Models, Large Language Models (LLMs), Generative Pre-trained Transformers (GPTs), embeddings, and Retrieval-Augmented Generation (RAG)\u003c\/li\u003e\n\u003cli\u003eDiscover how organizations combine traditional Machine Learning with Generative AI and AI agents to solve real-world business problems\u003c\/li\u003e\n\u003cli\u003eUnderstand why Responsible AI, explainability, fairness, privacy, and governance are becoming core competencies for modern Data Scientists\u003c\/li\u003e\n\u003cli\u003eExamine how an AI Assistant might be used in writing Python code and suggesting analysis approaches\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Data Preparation and Exploratory Analysis for Machine Learning and AI\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eBuild practical Python skills used by today’s Data Scientists, Machine Learning Engineers, and AI practitioners\u003c\/li\u003e\n\u003cli\u003eUse Python’s pandas library to explore, transform, combine, and prepare data for Machine Learning and AI applications\u003c\/li\u003e\n\u003cli\u003eApply Exploratory Data Analysis (EDA) techniques to uncover patterns, trends, anomalies, and business insights\u003c\/li\u003e\n\u003cli\u003eAssess data quality and address common challenges such as missing values, duplicates, outliers, normalization, and feature scaling\u003c\/li\u003e\n\u003cli\u003eExplore how EDA can help identify bias, fairness concerns, and model readiness before building Machine Learning models\u003c\/li\u003e\n\u003cli\u003eCreate effective visualizations using pandas, Matplotlib, and Seaborn to support data exploration, communication, and decision-making\u003c\/li\u003e\n\u003cli\u003eExamine how an AI Assistant might be used as a Personal Tutor or for Code Documentation\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Pre-processing Unstructured Data\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExplore how Data Scientists work with unstructured data such as text, images, audio, and video to support business decision-making\u003c\/li\u003e\n\u003cli\u003eLearn the fundamentals of Natural Language Processing (NLP), including text preprocessing, tokenization, feature engineering, vectorization, and text classification\u003c\/li\u003e\n\u003cli\u003eCompare traditional NLP techniques such as TF-IDF and term-document matrices with modern approaches based on embeddings, transformers, and Large Language Models (LLMs)\u003c\/li\u003e\n\u003cli\u003eGenerate and analyze embeddings using modern AI models, and discover how semantic similarity powers search, recommendation systems, Retrieval-Augmented Generation (RAG), and AI assistants\u003c\/li\u003e\n\u003cli\u003eUse Python libraries, OpenAI APIs, and Generative AI tools to prepare, analyze, and extract insights from unstructured data for Machine Learning and AI applications\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Predictive Analytics and Explainable AI with Linear Regression\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eLearn how to translate business forecasting problems into regression-based Machine Learning models and predictive analytics solutions\u003c\/li\u003e\n\u003cli\u003eBuild and train linear regression models in Python to predict continuous outcomes such as revenue, sales, costs, pricing, and resource consumption\u003c\/li\u003e\n\u003cli\u003eExplore simple and multiple linear regression techniques, including how model coefficients can be used to understand relationships between variables\u003c\/li\u003e\n\u003cli\u003eEvaluate model quality using statistical measures, residual analysis, and visualization techniques to assess predictive performance\u003c\/li\u003e\n\u003cli\u003eUnderstand the assumptions behind linear regression and learn how data quality, correlation, and feature selection can influence model accuracy\u003c\/li\u003e\n\u003cli\u003eDiscover why interpretable models remain important in modern AI systems, helping organizations explain predictions, support decision-making, and meet governance requirements\u003c\/li\u003e\n\u003cli\u003eUse a Generative AI Assistant to help explain the Regression findings to a non-technical audience\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Classification, Decision Trees, and Explainable AI\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eLearn how classification models predict categorical outcomes and support decision-making across business, healthcare, finance, security, and AI applications\u003c\/li\u003e\n\u003cli\u003eExplore the fundamentals of supervised learning, including training and test datasets, target variables, and predictor features used to build classification models\u003c\/li\u003e\n\u003cli\u003eBuild and apply decision tree classifiers that use recursive partitioning to categorize data and generate transparent, rule-based predictions\u003c\/li\u003e\n\u003cli\u003eEvaluate classification model performance using confusion matrices, accuracy and error rates while understanding the impact of class imbalance on results\u003c\/li\u003e\n\u003cli\u003eCompare common classification algorithms, including Decision Trees, Logistic Regression, Support Vector Machines, Random Forests, Neural Networks, and modern AI classification models\u003c\/li\u003e\n\u003cli\u003eExamine the growing importance of explainable AI, learning how decision trees provide transparent reasoning that can be understood, validated, and audited by business and regulatory stakeholders\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Alternative Approaches to Classification and Model Evaluation  \u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExplore alternative classification techniques beyond decision trees, including Logistic Regression, Neural Networks, and Naive Bayes\u003c\/li\u003e\n\u003cli\u003eLearn how Logistic Regression predicts binary outcomes by estimating probabilities, making it a widely used and highly interpretable classification method in business, healthcare, finance, and risk analytics\u003c\/li\u003e\n\u003cli\u003eConsidering how Activation Functions are integral to Logistic Regression Classifiers\u003c\/li\u003e\n\u003cli\u003eDelve into the architecture of Neural Networks and investigate the explosive growth of Deep Learning and Transformer approaches in AI\u003c\/li\u003e\n\u003cli\u003eExploring the probability foundations of Naive Bayes classifiers\u003c\/li\u003e\n\u003cli\u003eReviewing additional approaches to assess model performance: Precision, Recall, F1, ROC curves, and AUC metrics\u003c\/li\u003e\n\u003cli\u003eUse an AI assistant to visualize a neural network\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 7: Unsupervised Learning, Clustering, and Pattern Discovery in AI\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand clustering as an unsupervised learning technique that identifies natural groupings within data without predefined labels, making it useful for discovering hidden patterns and relationships\u003c\/li\u003e\n\u003cli\u003eExplore the concept of similarity and learn how clustering groups observations that are alike while separating observations that are significantly different based on their characteristics\u003c\/li\u003e\n\u003cli\u003eExamine various distance measures, including Euclidean and correlation-based distances, and understand how the choice of similarity metric can significantly influence clustering outcomes\u003c\/li\u003e\n\u003cli\u003eApply K-Means clustering to partition data into meaningful clusters through centroid initialization, cluster assignment, and iterative centroid updates until stable groupings are achieved\u003c\/li\u003e\n\u003cli\u003eLearn the importance of feature scaling and data preparation to ensure variables are comparable and clustering algorithms produce accurate and meaningful results\u003c\/li\u003e\n\u003cli\u003eExplore Hierarchical Clustering methods and understand how clustering supports business analytics, customer segmentation, recommendation systems, anomaly detection, and modern AI applications involving embeddings and similarity search\u003c\/li\u003e\n\u003cli\u003eUse an AI Assistant to help describe the characteristics of clusters from the centroid values\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 8: Association Rule Mining and AI-Powered Recommendation Systems\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand association rules as an unsupervised learning technique used to identify relationships between items that frequently occur together in transaction data, helping organizations uncover hidden patterns without predefined target variables\u003c\/li\u003e\n\u003cli\u003eLearn how association analysis supports business decision-making through market basket analysis, product placement, cross-selling, recommendation systems, fraud detection, website optimization, healthcare analytics, and manufacturing quality control.\u003c\/li\u003e\n\u003cli\u003eExplore how organizations can find meaningful patterns in anonymous transaction data, using purchase histories and item combinations to generate actionable insights even when customer identities are unavailable\u003c\/li\u003e\n\u003cli\u003eUnderstand how transaction datasets are transformed into sparse matrices and one-hot encoded representations to efficiently analyze large numbers of products and transactions within Python\u003c\/li\u003e\n\u003cli\u003eApply the Apriori algorithm to discover frequent itemsets by using minimum support thresholds that reduce computational complexity while identifying the most relevant item combinations\u003c\/li\u003e\n\u003cli\u003eEvaluate association rules using key metrics such as support, confidence, and lift, and learn how these measures help determine the strength, reliability, and usefulness of discovered relationships for recommendation systems and business analytics\u003c\/li\u003e\n\u003cli\u003eUse an AI Assistant to help customise Python code\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 9: Network Analysis\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand network analysis as a graph theory–based approach for studying relationships and interactions among entities, where nodes can represent people, objects, organizations, concepts, or systems and edges represent connections between them\u003c\/li\u003e\n\u003cli\u003eExplore how networks are represented as data structures, including nodes, edges, edge weights, and node attributes, enabling analysts to model relationships such as social connections, business transactions, information exchange, supply chains, and communication networks\u003c\/li\u003e\n\u003cli\u003eLearn to create and manage network graphs using Python’s NetworkX library, including building graphs manually, importing graph data from CSV and GML files, assigning node attributes, and accessing graph properties for analysis\u003c\/li\u003e\n\u003cli\u003eDevelop techniques for visualizing network relationships, using graph layouts, node sizing, coloring, labeling, and interactive visualization tools to reveal patterns, clusters, influential entities, and hidden structures within complex datasets\u003c\/li\u003e\n\u003cli\u003eApply egocentric network analysis to examine the relationships surrounding a specific individual or entity, helping identify local influence, connectivity, and interaction patterns within a network\u003c\/li\u003e\n\u003cli\u003eUse sociocentric network analysis to evaluate entire network structures, uncovering central actors, community structures, network resilience, information flow patterns, and organizational dynamics across complete systems\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 10: Communication, Deployment, Ethics, and the Future of Data Science in the AI Era\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eReview the complete data science life cycle, including business understanding, data acquisition and exploration, data preparation and transformation, model building, model evaluation, communicating results, and deployment\/operationalization. Understand how these stages work together in an iterative process for solving real-world business problems\u003c\/li\u003e\n\u003cli\u003eUnderstand the challenges of scaling and deploying machine learning and AI systems, including production pipelines, cloud versus on-premise deployment, monitoring model drift, latency considerations, governance, responsible AI practices, and human-in-the-loop systems\u003c\/li\u003e\n\u003cli\u003eDevelop skills in communicating data science results effectively, emphasizing storytelling, visualization, business interpretation, and explaining model limitations, uncertainty, confidence levels, ethical implications, and AI-generated outputs to technical and non-technical audiences\u003c\/li\u003e\n\u003cli\u003eExplore the role of data visualization in decision-making, including choosing appropriate chart types for relationships, comparisons, distributions, and compositions, while distinguishing between exploratory visualizations and presentation-focused visualizations designed for broader audiences\u003c\/li\u003e\n\u003cli\u003eExamine the future of data science in the Generative AI era, recognizing how AI is automating technical tasks while increasing the importance of business understanding, critical thinking, communication, ethical decision-making, and the ability to interpret and operationalize AI-driven insights\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"269A63US \/ 2026-09-14T09:00:00 \/ Online","offer_id":44282810663048,"sku":"US-1264-IL","price":2219.0,"currency_code":"USD","in_stock":true},{"title":"269C26US \/ 2026-09-21T09:00:00 \/ Online","offer_id":44748276531336,"sku":"US-1264-IL","price":2219.0,"currency_code":"USD","in_stock":true},{"title":"26AA03CN \/ 2026-10-05T09:00:00 \/ Toronto","offer_id":44748276564104,"sku":"US-1264-IL","price":2219.0,"currency_code":"USD","in_stock":true},{"title":"26AA81US \/ 2026-10-19T09:00:00 \/ New 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