Introduction to Data Science, Machine Learning & AI using Python

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
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• 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