{"product_id":"introduction-to-ai-data-science-machine-learning-with-python","title":"Introduction to AI, Data Science \u0026 Machine Learning with Python","description":"\u003cdiv\u003e\n\u003cp\u003eData science is a field that has exploded in popularity in recent years, and for good reason. Companies across industries are increasingly relying on data to inform their decision-making, and skilled data scientists are in high demand. In this comprehensive course, you'll learn the foundational skills and techniques you need to succeed in this exciting field.\u003c\/p\u003e\r\n\u003cp\u003eYou'll start by exploring the role of a data scientist and the lifecycle of data science efforts within an organization. Then, you'll dive into the technical skills you need, such as using Python and its relevant libraries for data analysis and visualization, preprocessing unstructured data, and building AI\/ML models.\u003c\/p\u003e\r\n\u003cp\u003eYou'll also explore key machine learning algorithms, including linear regression, decision tree classifiers, and clustering algorithms. And, you'll learn how to apply these techniques to real-world problems, such as predicting customer churn and building recommendation engines.\u003c\/p\u003e\r\n\u003cp\u003eThroughout the \u003cstrong\u003edata science training\u003c\/strong\u003e, you'll have the opportunity to work on hands-on exercises and projects, allowing you to practice your skills and build your portfolio. By the end of the course, you'll have a deep 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.\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\u003eConsider how traditional Predictive Machine Learning techniques combine Generative AI, Agentic AI, Multimodal AI and Deepseek-R1 approaches in the current Data Science landscape.\u003c\/li\u003e\n\u003cli\u003eTranslate everyday business questions and problems into Machine Learning tasks to make data-driven decisions.\u003c\/li\u003e\n\u003cli\u003eUse Python Pandas, Matplotlib \u0026amp; Seaborn libraries to explore, analyze, and visualize data from various sources, including the web, word documents, email, NoSQL stores, databases, and data warehouses.\u003c\/li\u003e\n\u003cli\u003e\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003eExplore the concepts behind Foundation Models, Generative Pre-trained Transformers (GPTs), and Retrieval Augmented Generation (RAGs).\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003eTrain a Machine Learning Classifier using different algorithmic techniques from the Scikit-Learn library, such as Decision Trees, Logistic Regression, and Neural Networks.\u003c\/li\u003e\n\u003cli\u003eRe-segment your customer market using K-Means and Hierarchical algorithms to better align products and services to customer needs.\u003c\/li\u003e\n\u003cli\u003eDiscover hidden customer behaviors from Association Rules and build a Recommendation Engine based on behavioral patterns.\u003c\/li\u003e\n\u003cli\u003eInvestigate relationships \u0026amp; flows between people and business-relevant entities using Social Network Analysis.\u003c\/li\u003e\n\u003cli\u003eBuild predictive models of revenue and other numeric variables using Linear Regression.\u003c\/li\u003e\n\u003cli\u003eTest your knowledge with the included end-of-course exam.\u003c\/li\u003e\n\u003cli\u003eLeverage continued support with after-course one-on-one instructor coaching and computing sandbox.\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 Role of a Data Scientist: Combining Technical and Non-Technical Skills\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eWhat is the required skillset of a Data Scientist?\u003c\/li\u003e\n\u003cli\u003eCombining the technical and non-technical roles of a Data Scientist\u003c\/li\u003e\n\u003cli\u003eThe difference between a Data Scientist and a Data Engineer\u003c\/li\u003e\n\u003cli\u003eExploring the entire lifecycle of Data Science efforts within the organization\u003c\/li\u003e\n\u003cli\u003eTurning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models\u003c\/li\u003e\n\u003cli\u003eExploring diverse and wide-ranging data sources that you can use to answer business questions\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003eExplore the concepts behind Foundation Models, Generative Pre-trained Transformers (GPTs), and Retrieval Augmented Generation (RAGs)\u003c\/span\u003e\u003cspan data-ccp-props=\"{}\"\u003e \u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Data Manipulation and Visualization using Python's Pandas and Matplotlib Libraries\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eIntroducing the features of Python that are relevant to Data Scientists and Data Engineers\u003c\/li\u003e\n\u003cli\u003eViewing Data Sets using Python’s Pandas library\u003c\/li\u003e\n\u003cli\u003eImporting, exporting, and working with all forms of data, from Relational Databases to Google Images\u003c\/li\u003e\n\u003cli\u003eUsing Python Selecting, Filtering, Combining, Grouping, and Applying Functions from Python's Pandas library\u003c\/li\u003e\n\u003cli\u003eDealing with Duplicates, Missing Values, Rescaling, Standardizing, and Normalizing Data\u003c\/li\u003e\n\u003cli\u003eVisualizing data for both exploration and communication with the Pandas, Matplotlib, and Seaborn Python libraries\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Preprocessing and Analyzing Unstructured Data with Natural Language Processing\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003ePreprocessing Unstructured Data such as web adverts, emails, and blog posts for AI\/ML models\u003c\/li\u003e\n\u003cli\u003eExploring the most popular approaches to Natural Language Processing (NLP), such as stemming and \"stop\" words\u003c\/li\u003e\n\u003cli\u003ePreparing a term-document matrix (TDM) of unstructured documents for analysis\u003c\/li\u003e\n\u003cli\u003e\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003eReview the architectures of Foundation Models, Generative Pre-trained Transformers (GPTs), and Retrieval Augmented Generation (RAGs)\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003eLook at how Data Scientists can integrate Large Language Models (LLMs) in their work\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Linear Regression and Feature Engineering for Business Problem Solving\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExpressing a business problem, such as customer revenue prediction, as a linear regression task\u003c\/li\u003e\n\u003cli\u003eAssessing variables as potential Predictors of the required Target (e.g., Education as a predictor of Salary Build)\u003c\/li\u003e\n\u003cli\u003eInterpreting and Evaluating a Linear Regression model in Python using measures such as RMSE\u003c\/li\u003e\n\u003cli\u003eExploring the Feature Engineering possibilities to improve the Linear Regression model\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Classification Models and Evaluation for Predictive Analysis\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eLearning how AI\/ML Classifiers are built and used to make predictions such as Customer Churn\u003c\/li\u003e\n\u003cli\u003eExploring how AI\/ML Classification models are built using Training, Test, and Validation\u003c\/li\u003e\n\u003cli\u003eEvaluating the strength of a Decision Tree Classifier\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\u003eExamining alternative approaches to classification\u003c\/li\u003e\n\u003cli\u003eConsidering how Activation Functions are integral to Logistic Regression Classifiers\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003eDelve into the architecture of Neural Networks and investigate the explosive growth of Deep Learning approaches in AI\u003c\/span\u003e\u003cspan data-ccp-props=\"{}\"\u003e \u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003eExploring the probability foundations of Naive Bayes classifiers\u003c\/li\u003e\n\u003cli\u003eReviewing different approaches to measuring the performance of AI\/ML Classification Models\u003c\/li\u003e\n\u003cli\u003eReviewing ROC curves, AUC measures, Precision, Recall, and Confusion Matrices\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 7: Clustering Techniques for Customer and Product Segmentation\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eUncovering new ways of segmenting your customers, products, or services using clustering algorithms\u003c\/li\u003e\n\u003cli\u003eExploring what the concept of similarity means to humans and how you can implement it programmatically through distance measures on descriptive variables\u003c\/li\u003e\n\u003cli\u003ePerforming top-down clustering with Python’s Scikit-Learn K-Means algorithm\u003c\/li\u003e\n\u003cli\u003ePerforming bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm\u003c\/li\u003e\n\u003cli\u003eExamining clustering techniques on unstructured data (e.g., Tweets, Emails, Documents, etc.)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 8: Association Rules and Recommender Systems for Business Applications\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eBuilding models of customer behaviors or business events from logged data using Association Rules\u003c\/li\u003e\n\u003cli\u003eEvaluating the strength of these models through probability measures of support, confidence, and lift\u003c\/li\u003e\n\u003cli\u003eEmploying feature engineering approaches to improve the models\u003c\/li\u003e\n\u003cli\u003eBuilding a recommender for your customers that is unique to your product\/service offering\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 9: Network Analysis for Organizational Insights\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eAnalyzing your organization, its people, and its environment as a network of inter-relationships\u003c\/li\u003e\n\u003cli\u003eVisualizing these relationships to uncover previously unseen business insights\u003c\/li\u003e\n\u003cli\u003eExploring ego-centric and socio-centric methods of analyzing connections critical to your organization\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 10: Big Data Analytics, Communication, and Ethics\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eExamining Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics\u003c\/li\u003e\n\u003cli\u003eExploring the communications and ethics aspects of being a Data Scientist\u003c\/li\u003e\n\u003cli\u003eDiscuss the ethical implications of recent developments in AI\u003c\/li\u003e\n\u003cli\u003eSurveying the paths of continual learning for a Data Scientist\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"267A76US \/ 2026-07-13T09:00:00 \/ Bellevue, WA","offer_id":47534164705499,"sku":"US-1264-IL","price":2680.0,"currency_code":"USD","in_stock":true},{"title":"267A77US \/ 2026-07-27T09:00:00 \/ New York","offer_id":47534164738267,"sku":"US-1264-IL","price":2680.0,"currency_code":"USD","in_stock":true},{"title":"268A50US \/ 2026-08-24T09:00:00 \/ 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