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Introduction to AI, Data Science & Machine Learning with Python

Course 1264

  • Duration: 5 days
  • Sandbox: Yes
  • Language: English
  • Level: Foundation

Data 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.

You'll start by exploring the role of a data scientist and the lifecycle of data science efforts within an organisation. Then, you'll dive into the technical skills you need, such as using Python and its relevant libraries for data analysis and visualisation, preprocessing unstructured data, and building AI/ML models.

You'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.

Throughout the data science training, 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.

Data Science Training in Python Delivery Methods

  • In-Person

  • Online

Data Science Training in Python Course Information

In this course, you will:

  • 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, analyse, and visualise 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 behaviours from Association Rules and build a Recommendation Engine based on behavioural 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.
  • Test your knowledge with the included end-of-course exam.
  • Leverage continued support with after-course one-on-one instructor coaching and computing sandbox.

Training Prerequisites

None.

Data Science Training in Python Course Outline

  • 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
  • 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, Standardising, and Normalising Data
  • Visualising data for both exploration and communication with the Pandas, Matplotlib, and Seaborn Python libraries
  • 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
  • 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
  • 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
  • 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
  • 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.)
  • 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
  • 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
  • Examining Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics
  • Exploring the communications and ethics aspects of being a Data Scientist
  • Discuss the ethical implications of recent developments in AI
  • Surveying the paths of continual learning for a Data Scientist
Get This Course 36 330 kr For Team Prices Call: +46 (0)8 506 668 00

  • 5-day instructor-led training course

  • After-course coaching available

  • After-course computing sandbox access included

  • End-of-course exam included

Get the Advantage 35 495 kr
  • Take this course and gain unlimited access to more than 300 virtual instructor-led courses
  • Future-proof your career with more than 100 sought-after certifications in the market
  • Build real skills through hands-on learning in more than 180 virtual labs
  • Grow your skills and capabilities with more than one course at a time and save
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  • Guaranteed to Run - you can rest assured that the class will not be cancelled.
    apr 22 - 26 15:00 - 22:30 CEST
    Virtual
  • maj 13 - 17 9:00 - 16:30 CEST
    Stockholm or Virtual
  • Guaranteed to Run - you can rest assured that the class will not be cancelled.
    maj 13 - 17 10:00 - 17:30 CEST
    London or Virtual
  • jun 3 - 7 15:00 - 22:30 CEST
    Ottawa or Virtual
  • jun 10 - 14 10:00 - 17:30 CEST
    London or Virtual
  • jun 10 - 14 9:00 - 16:30 CEST
    Stockholm or Virtual
  • jun 24 - 28 15:00 - 22:30 CEST
    Virtual
  • jul 15 - 19 16:00 - 23:30 CEST
    Austin or Virtual
  • jul 22 - 26 9:00 - 16:30 CEST
    Stockholm or Virtual
  • jul 22 - 26 10:00 - 17:30 CEST
    London or Virtual
  • jul 22 - 26 15:00 - 22:30 CEST
    Virtual
  • jul 29 - aug 2 15:00 - 22:30 CEST
    New York or Virtual
  • aug 19 - 23 15:00 - 22:30 CEST
    Toronto or Virtual
  • aug 26 - 30 15:00 - 22:30 CEST
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  • sep 9 - 13 10:00 - 17:30 CEST
    London or Virtual
  • sep 9 - 13 9:00 - 16:30 CEST
    Stockholm or Virtual
  • sep 9 - 13 15:00 - 22:30 CEST
    Virtual
  • sep 16 - 20 15:00 - 22:30 CEST
    Herndon, VA or Virtual
  • sep 23 - 27 15:00 - 22:30 CEST
    Virtual
  • okt 7 - 11 10:00 - 17:30 CEST
    London or Virtual
  • okt 7 - 11 9:00 - 16:30 CEST
    Stockholm or Virtual
  • okt 28 - nov 1 14:00 - 21:30 CET
    Ottawa or Virtual
  • nov 11 - 15 10:00 - 17:30 CET
    London or Virtual
  • nov 11 - 15 9:00 - 16:30 CET
    Stockholm or Virtual
  • dec 9 - 13 16:00 - 23:30 CET
    Austin or Virtual
  • dec 16 - 20 10:00 - 17:30 CET
    London or Virtual
  • dec 16 - 20 9:00 - 16:30 CET
    Stockholm or Virtual
  • jan 6 - 10 15:00 - 22:30 CET
    New York or Virtual
  • jan 20 - 24 10:00 - 17:30 CET
    London or Virtual
  • jan 20 - 24 9:00 - 16:30 CET
    Stockholm or Virtual
  • jan 27 - 31 15:00 - 22:30 CET
    Toronto or Virtual
  • feb 10 - 14 15:00 - 22:30 CET
    Herndon, VA or Virtual
  • feb 24 - 28 10:00 - 17:30 CET
    London or Virtual
  • feb 24 - 28 9:00 - 16:30 CET
    Stockholm or Virtual

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  • Bring this or any training to your organisation

  • Full-scale programme development

  • Delivered when, where, and how you want it

  • Blended learning models

  • Tailored content

  • Expert team coaching

Get the Advantage 35 495 kr
  • Take this course and gain unlimited access to more than 300 virtual instructor-led courses
  • Future-proof your career with more than 100 sought-after certifications in the market
  • Build real skills through hands-on learning in more than 180 virtual labs
  • Grow your skills and capabilities with more than one course at a time and save
Thank you for choosing subscriptions!
#1264
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Data Science Training in Python Course FAQs

The AI ML Data Science Python Course is a training course that teaches participants how to use Python libraries to build, evaluate, and deploy Machine Learning (ML) and Artificial Intelligence (AI) models to gain insights from data.

This course covers every stage of the Data Science Lifecycle and teaches you how to manage, transform, and visualise raw data to create predictive models to help you find and evaluate future opportunities. You will also learn how to translate everyday business questions and problems into Machine Learning tasks to make data-driven decisions.

This course covers a wide range of topics, including data analysis, structured and unstructured data, natural language processing, sentiment analysis, data points, customer segments, logistic regression, random forests, nearest neighbors, business intelligence, target customers, data models, trading strategies, marketing campaigns, and programming language.

This course is designed for anyone who wants to gain foundational knowledge of data science, including both technical and non-technical beginners. It is particularly relevant for data scientists, analysts, and other professionals who work with data and want to improve their skills.

This course helps you gain business intelligence by teaching you how to uncover previously unseen business insights. You will analyse your organisation, its people, and its environment as a network of inter-relationships and visualise these relationships to gain insights.

You will also learn how to build predictive models of revenue and other numeric variables using linear regression and how to use these models to inform business decisions.

Schedules are busy, but big data training online makes it easy to level up your career. If you need Big Data online training, we've got you covered. Our AnyWare course delivery option gives you the advantages of a live classroom right from the comfort of your computer screen–no matter where you are.

Yes. There are various opportunities to build a model and analyse issues throughout the training.

You just need an interest in gaining foundational knowledge of data science. This data scientist training course is designed for technical and non-technical beginners.

Marketing strategy is an important topic in this course as it teaches participants how to uncover new ways of segmenting customers, products, or services using clustering algorithms.

You will also learn how to build models of customer behaviours or business events from logged data using association rules and how to use this information to build a recommender for your customers that is unique to your product/service offering.

Natural language processing is used in this course to preprocess unstructured data such as web adverts, emails, and blog posts for AI/ML models. You will explore the most popular approaches to NLP, such as stemming and "stop" words, and learn how to prepare a term-document matrix (TDM) of unstructured documents for analysis.

This course covers a variety of data models, including linear regression, logistic regression, random forests, and nearest neighbors. You will learn how to express a business problem, such as customer revenue prediction, as a linear regression task and how to assess variables as potential predictors of the required target. You will also explore alternative approaches to classification, including neural networks and deep learning.

Sentiment analysis is used in this course to analyse customer feedback and opinions about products or services. You will learn how to perform sentiment analysis on customer reviews and use this information to inform marketing campaigns and trading strategies.

This course teaches participants how to work with both structured and unstructured data. You will learn how to use Python libraries to import, export, and work with all forms of data, from relational databases to Google Images. You will also explore clustering techniques on unstructured data such as tweets, emails, and documents.

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