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Introduction to Data Science, Machine Learning & AI using Python Training

Nivå: Foundation

If you want to become a Data Scientist, this is the place to begin! Introduction to Data Science, Machine Learning & AI (Python version) covers every stage of the Data Science Lifecycle, from working with raw datasets to building, evaluating and deploying Machine Learning (ML) and Artificial Intelligence (AI) models that create efficiencies for the organisation and lead to previously undiscovered insights from your data.

It begins by teaching you how to use Python libraries, such as Pandas, Numpy and SciPy, to work with all types of data in Python, including everything from data in a Relational Database to Google Images. You’ll learn how to manage, transform and visualise data in every conceivable way, in order to unearth the real value in your current and historic data. You’ll then use Python libraries such as Scikit- Learn to understand how to build, evaluate and deploy many Machine Learning (ML) and Artificial Intelligence (AI) models that not only predict into the future but constantly learn from data as new events unfold.

By the end, you will be able to confidently apply many ML & AI techniques to both enhance your organisation’s efficiencies and, through predictive modelling, be prepared for future possibilities.

You Will Learn How To

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Nyckelfunktioner:

  • Choose from blended on-demand and instructor-led learning options
  • Exclusive LinkedIn group membership for peer and SME community support
  • After-course instructor coaching benefit
  • Learning Tree end-of-course exam included
  • After-course computing sandbox included

Du kommer lära dig att:

  • Translate everyday business questions as well as more complex problems into Machine Learning tasks in order to make truly data-driven decisions
  • Use Python Pandas, Matplotlib & Seaborn libraries to Explore, Analyse & Visualise data from varied sources (the Web, Word documents, Email, Twitter, NoSQL stores, Databases, Data Warehouses & more) for patterns and trends relevant to your business
  • Train a Machine Learning Classifier using different algorithmic techniques from the Scikit-Learn library (eg. Decision Trees, Logistic Regression, Neural Networks)
  • Re-segment your customer market using K-Means & Hierarchical algorithms for better alignment of products & services to customer needs
  • Discover hidden customer behaviours from Association Rules and build a Recommendation Engine based on behavioral 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

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BLANDAT LÄRANDE

On-demand och online lärarträffar

Unlimited annual access to:

  • 12 eBooks
  • 2 on-demand video courses
  • 1-day instructor-led training course
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12900 SEK/ Year

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I klass & Live, Online-utbildning

  • 5-day instructor-led training course
  • One-on-one after-course instructor coaching
  • After-course computing sandbox access included
  • End-of-course exam included
  • Pay later by invoice -OR- at the time of checkout by credit card
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Klassrum, On-demand och självstudier

Unlimited annual access to:

  • 5-day instructor-led training course
  • 1-day instructor-led review session
  • 12 eBooks
  • 2 on-demand videos
  • One-on-one after-course instructor coaching
  • After-course computing sandbox access
  • End-of-course exam included
Se innehåll i självstudiekursen och datum

39000 SEK/Year

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PRODUCT #70I8

FÖRETAGSINTERN UTBILDNING

Teamträning

  • Använd denna eller någon annan utbildning i ditt företag
  • Fullskalig programutveckling
  • Levereras när, var och hur du vill
  • Blandade utbildningsmodellerSkräddarsytt innehåll
  • Coaching av ett expertteam

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Utveckla dig och ditt team med anpassade eller öppna kurser alternativt e-learning

Learning Tree erbjuder kundanpassad utbildning hos er, öppna kurser i Stockholm, London eller Washington, möjlighet att delta via våra Anywhere centers (Malmö, Göteborg, Linköping, Stockholm eller Borlänge) eller olika former av e-learning med lärarstöd. Läs mer på www.learningtree.se/priser .

On-demand och online lärarträffar

Important Data Science, Machine Learning & AI using Python Training Information

  • Data Science Blended Training Description

    The eBooks and on-demand courses provided with this offering are a great way to explore your interest in the topics covered in the instructor-led course. At any time during your annual access to this offering, you may attend one of our 1-day review sessions, Course 4509: Introduction to Python for Data Analytics.

On-Demand Training Content

  • eBooks

    • Managing Data Science 
    • Hands-On Data Analysis with Pandas 
    • Hands-On Natural Language Processing with Python
    • Data Science Algorithms in a Week
    • Hands-On Machine Learning for Algorithmic Trading
    • HandsOn Neural Networks 
    • Machine Learning Algorithms 
    • Machine Learning with scikit-learn Quick Start Guide 
    • Hands-On Data Science for Marketing 
    • Feature Engineering Made Easy 
    • Artificial Intelligence By Example
    • Hands-On Machine Learning on Google Cloud Platform

  • On-Demand Videos

    • Exploratory Data Analysis with Pandas and Python 3.x
    • Advanced Data Structures and Algorithms in Python

Introduction to Data Science, Machine Learning & AI using Python FAQs

  • What background do I need?

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

  • Is the on-demand content the same as the 5-day instructor class?

    No. While the content selected does map to the objectives of the instructor-led course, it does not include a recorded version of the instructor-led class. The objectives have been re-imagined to be presented in digital, self-guided formats.

  • What on-demand content will I receive?

    An outline of the content you will receive can be seen above. 

  • How will I access my course materials if I choose this method?

    Once payment is received, you will receive an email from Learning Tree with all the links and information you need to get started.

  • How can I sign up for a review session?

    Once you are enrolled in the program, specific details and dates will be sent to you.

One Day Instructor-Led Review

You'll be able to register for a Training Review Session at any time after you've placed your order.

  • 18 nov (1 dag)
    9:00 - 4:30 EST     Online (AnyWare) Online (AnyWare)
  • 23 nov (1 dag)
    9:00 - 4:30 GMT     Online (AnyWare) Online (AnyWare)
  • 3 feb (1 dag)
    9:00 - 4:30 EST     Online (AnyWare) Online (AnyWare)
  • 10 mar (1 dag)
    9:00 - 4:30 GMT     Online (AnyWare) Online (AnyWare)
  • 11 maj (1 dag)
    9:00 - 4:30 EDT     Online (AnyWare) Online (AnyWare)
  • 24 maj (1 dag)
    9:00 - 4:30 BST     Online (AnyWare) Online (AnyWare)
  • 16 aug (1 dag)
    9:00 - 4:30 BST     Online (AnyWare) Online (AnyWare)
  • 17 aug (1 dag)
    9:00 - 4:30 EDT     Online (AnyWare) Online (AnyWare)

I klass & Live, Online-utbildning

Note: This course runs for 5 dagar *

*Events with the Partial Day Event clock icon run longer than normal but provide the convenience of half-day sessions.

  • 15 - 19 mar 9:00 - 4:30 GMT Online (AnyWare) Online (AnyWare) Boka Din Kursplats

  • 7 - 11 jun 9:00 - 4:30 BST Online (AnyWare) Online (AnyWare) Boka Din Kursplats

  • 6 - 10 sep 9:00 - 4:30 BST Online (AnyWare) Online (AnyWare) Boka Din Kursplats

  • 28 sep - 2 okt 9:00 - 4:30 EDT Online (AnyWare) Online (AnyWare) Boka Din Kursplats

  • 2 - 6 nov 9:00 - 4:30 EST Online (AnyWare) Online (AnyWare) Boka Din Kursplats

  • 30 nov - 4 dec 9:00 - 4:30 EST Online (AnyWare) Online (AnyWare) Boka Din Kursplats

  • 11 - 15 jan 9:00 - 4:30 EST Toronto / Online (AnyWare) Toronto / Online (AnyWare) Boka Din Kursplats

  • 22 - 26 mar 9:00 - 4:30 EDT New York / Online (AnyWare) New York / Online (AnyWare) Boka Din Kursplats

  • 3 - 7 maj 9:00 - 4:30 EDT Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Boka Din Kursplats

  • 7 - 11 jun 9:00 - 4:30 EDT Ottawa / Online (AnyWare) Ottawa / Online (AnyWare) Boka Din Kursplats

  • 12 - 16 jul 9:00 - 4:30 EDT Toronto / Online (AnyWare) Toronto / Online (AnyWare) Boka Din Kursplats

Kurs med startgaranti

När du ser symbolen för “Guaranteed to Run” vid ett kurstillfälle vet du att kursen blir av. Garanterat.

Partial Day Event

Learning Tree offers a flexible schedule program. If you cannot attend full day sessions, this option consists of four-hour sessions per day instead of the full-day session.

Important Data Science, Machine Learning & AI using Python Course Information

  • Who Should Attend This Course: 

    This course is for anyone who is interested in gaining foundational knowledge of data science. This data scientist training course is designed for beginners, technical and non-technical.

Introduction to Data Science, Machine Learning & AI using Python Course Outline

  • Chapter 1

    • What is the required Skill-set of a Data Scientist
    • Combining the Technical and Non-technical roles of a Data Scientist
    • The difference between a Data Scientist and a Data Engineeer
    • Explore the full lifecycle of Data Science efforts within the organisation
    • Discuss how to turn business questions into Machine Learning (ML) and Artificial Intelligence (AI) models
    • Explore diverse and wide-ranging data sources, internal and external to the organisation that can be used to answer business questions

  • Chapter 2

    • Introduce the features of Python that make it an ideal tool for Data Scientists and Data Engineers alike
    • Viewing Data Sets using Python’s Pandas library
    • Importing, Exporting and working with all forms of Data, from Relational Databases to Google Images using the Python
    • Selecting, Filtering, Combining, Grouping and Applying Functions using 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

  • Chapter 3

    • Preprocess Unstructured Data such as web adverts, emails, blog posts, in order to use it our AI/ML models
    • Explore the most popular approaches to Natural Language Processing (NLP) such as stemming, and “stop” words
    • Prepare a term-document matrix (TDM) of unstructured documents in preparation for analysis

  • Chapter 4

    • Express a business problem such as customer revenue prediction as a linear regression task
    • Assess variables as potential Predictors of the required Target eg. Education as a predictor of Salary
    • Build, Interpret and Evaluate a Linear Regression model in Python using measures such as RMSE
    • Explore the Feature Engineering possibilities to improve the Linear Regression model

  • Chapter 5

    • Learn how AI/ML Classifiers are built and used to make predictions such as Customer Churn
    • Explore how AI/ML Classification models are built using Training, Test and Validation Datasets
    • Build, Apply and Evaluate the strength of a Decision Tree Classifier

  • Chapter 6

    • Examine some alternative approaches to classification
    • Consider how Activation Functions are integral to Logistic Regression Classifiers
    • Investigate how Neural Networks and Deep Learning are used to build self-driving cars
    • Explore the probability foundations of Naive Bayes classifiers
    • Review different approaches to measuring the performance of AI/ML Classification Models
    • ROC curves, AUC measures, Precision, Recall, Confusion Matrix
    • \

  • Chapter 7

    • Uncover new ways of segmenting your customers, products or services through the use of clustering algorithms
    • Explore what the concept of similarity means to humans and how it can be implemented programmatically through distance measures on descriptive variables
    • Perform top-down clustering with Python’s Scikit-Learn K-Means algorithm
    • Perform bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm
    • Examine clustering techniques on unstructured data (eg. Tweets, Emails, Documents, etc)

  • Chapter 8

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

  • Chapter 9

    • Analyse your organisation, its people and environment as a network of inter-relationships
    • Visualise these relationships to uncover previously unseen business insights
    • Explore ego-centric and socio-centric methods of analysing connections important to your organisation

  • Chapter 10

    • Examine Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics
    • Explore the communications and ethics aspects of being a Data Scientist
    • Survey the paths of continual learning for a Data Scientist

Introduction to Data Science, Machine Learning & AI using Python FAQs

  • What background do I need?

    There are no expectations regarding specific platforms except basic familiarity with a Windows environment. It’s designed for beginners, technical and non-technical.

  • Does this include any practical, hands-on learning?

    Yes. There are various opportunities to build model and analyses issues throughout the period of the training.

  • Does Learning Tree offer Big data online training?

    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.

Klassrum, On-demand och självstudier

Note: This course runs for 5 dagar *

*Events with the Partial Day Event clock icon run longer than normal but provide the convenience of half-day sessions.

  • 15 - 19 mar 9:00 - 4:30 GMT Online (AnyWare) Online (AnyWare)

  • 7 - 11 jun 9:00 - 4:30 BST Online (AnyWare) Online (AnyWare)

  • 6 - 10 sep 9:00 - 4:30 BST Online (AnyWare) Online (AnyWare)

  • 28 sep - 2 okt 9:00 - 4:30 EDT Online (AnyWare) Online (AnyWare)

  • 2 - 6 nov 9:00 - 4:30 EST Online (AnyWare) Online (AnyWare)

  • 30 nov - 4 dec 9:00 - 4:30 EST Online (AnyWare) Online (AnyWare)

  • 11 - 15 jan 9:00 - 4:30 EST Toronto / Online (AnyWare) Toronto / Online (AnyWare)

  • 22 - 26 mar 9:00 - 4:30 EDT New York / Online (AnyWare) New York / Online (AnyWare)

  • 3 - 7 maj 9:00 - 4:30 EDT Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare)

  • 7 - 11 jun 9:00 - 4:30 EDT Ottawa / Online (AnyWare) Ottawa / Online (AnyWare)

  • 12 - 16 jul 9:00 - 4:30 EDT Toronto / Online (AnyWare) Toronto / Online (AnyWare)

Kurs med startgaranti

När du ser symbolen för “Guaranteed to Run” vid ett kurstillfälle vet du att kursen blir av. Garanterat.

Partial Day Event

Learning Tree offers a flexible schedule program. If you cannot attend full day sessions, this option consists of four-hour sessions per day instead of the full-day session.

Important Data Science, Machine Learning & AI using Python Training Information

  • Data Science Blended Training Description

    The eBooks and on-demand courses provided as with this offering are a great way to explore your interest in the topics covered in the instructor-led course. At any time during your annual access to this offering, you may attend the 5-day instructor-led course or one of our 1-day review sessions, Course 4509: Introduction to Python for Data Analytics.

  • How to Schedule Your Instructor-Led Training

    Once payment is received, you will receive details for your Unlimited Access Training Bundle via email. At that time, you may call or email our customer service team for assistance in enrolling in the event date of your choice.

On-Demand Training Content

  • eBooks

    • Managing Data Science 
    • Hands-On Data Analysis with Pandas 
    • Hands-On Natural Language Processing with Python
    • Data Science Algorithms in a Week
    • Hands-On Machine Learning for Algorithmic Trading
    • HandsOn Neural Networks 
    • Machine Learning Algorithms 
    • Machine Learning with scikit-learn Quick Start Guide 
    • Hands-On Data Science for Marketing 
    • Feature Engineering Made Easy 
    • Artificial Intelligence By Example
    • Hands-On Machine Learning on Google Cloud Platform

  • On-Demand Videos

    • Exploratory Data Analysis with Pandas and Python 3.x
    • Advanced Data Structures and Algorithms in Python

Introduction to Data Science, Machine Learning & AI using Python FAQs

  • What background do I need?

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

  • Is the on-demand content the same as the 5-day instructor class?

    No. While the content selected does map to the objectives of the instructor-led course, it does not include a recorded version of the instructor-led class. The objectives have been re-imagined to be presented in digital, self-guided formats.

  • What on-demand content will I receive?

    An outline of the content you will receive can be seen above. 

  • How will I access my course materials if I choose this method?

    Once payment is received, you will receive an email from Learning Tree with all the links and information you need to get started.

  • How can I sign up for a review session?

    Once you are enrolled in the program, specific details and dates will be sent to you.

  • How do I schedule my instructor-led training?

    Once payment is received, you will receive details for your Unlimited Access Training Bundle via email. At that time, you may call or email our customer service team for assistance in enrolling in the event date of your choice.

One Day Instructor-Led Review

You'll be able to register for a Training Review Session at any time after you've placed your order.

  • 18 nov (1 dag)
    9:00 - 4:30 EST     Online (AnyWare) Online (AnyWare)
  • 23 nov (1 dag)
    9:00 - 4:30 GMT     Online (AnyWare) Online (AnyWare)
  • 3 feb (1 dag)
    9:00 - 4:30 EST     Online (AnyWare) Online (AnyWare)
  • 10 mar (1 dag)
    9:00 - 4:30 GMT     Online (AnyWare) Online (AnyWare)
  • 11 maj (1 dag)
    9:00 - 4:30 EDT     Online (AnyWare) Online (AnyWare)
  • 24 maj (1 dag)
    9:00 - 4:30 BST     Online (AnyWare) Online (AnyWare)
  • 16 aug (1 dag)
    9:00 - 4:30 BST     Online (AnyWare) Online (AnyWare)
  • 17 aug (1 dag)
    9:00 - 4:30 EDT     Online (AnyWare) Online (AnyWare)

Teamträning

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