25,000+ Courses Nationwide
0203 908 2376

Introduction to Data Science, Machine Learning & AI using Python

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.

Key Features of this Data Science, Machine Learning & AI using Python Training:

  • 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
  • End-of-course exam included
  • After-course computing sandbox included

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.

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.

Select specific date to see price, venue and full details.

Learning Objectives

  • 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


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.

Course Content

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 popularapproaches to Natural Language Processing (NLP) such as stemming, and “stop” words
  • Prepare a term-document matrix (TDM) of unstructured documents in preparation foranalysis

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 fora Data Scientist

Related Courses

Privacy Notice

In order to provide you with the service requested we will need to retain and use your contact information in accordance with our Privacy Notice. If you choose to provide us with this information you explicitly consent to us using the information as necessary to provide the requested service to you. If you do not agree please do not proceed to request the service from us.

Marketing Permissions

Would you like to receive our newsletter and other information on products and services which we think will be of interest to you by email. We will always treat your information with care and in accordance with our Privacy Notice. You are free to withdraw this permission at any time.


We work with the best