Introduction to Data Science, Machine Learning & AI

Introduction to Data Science, Machine Learning & AI using Python

In this Data Science Training in Python course, you will learn how to use Python libraries to build, evaluate, and deploy Machine Learning (ML) and Artificial Intelligence (AI) models that can help you gain previously uncovered insights from your data.

This course covers every stage of the Data Science Lifecycle and teaches you how to manage, transform, and visualize raw data to create predictive models that will help you find and evaluate future opportunities.

Data Science Training in Python Benefits

• Translate everyday business questions and problems into Machine Learning tasks to make data-driven decisions
• Use Python Pandas, Matplotlib & Seaborn libraries to explore, analyze, and visualize 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 for better alignment of products and services to customer needs
• Discover hidden customer behaviors 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
• Gain access to an exclusive LinkedIn group for peer and community support
• Test your knowledge with the included end-of-course exam
• Leverage continued support with after-course one-on-one instructor coaching and computing sandbox

Data Science in Python Instructor-Led Course Outline

Module 1

• 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 full lifecycle of Data Science efforts within the organization
• Turning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models
• Exploring diverse and wide-ranging data sources that can be used to answer business questions

Module 2

• 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, Standardizing and Normalizing Data
• Visualizing data for both exploration and communication with the Pandas, Matplotlib and Seaborn Python libraries

Module 3

• 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

Module 4

• 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

Module 5

• 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

Module 6

• 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, Confusion Matrices

Module 7

• Uncovering new ways of segmenting your customers, products, or services using clustering algorithms
• Exploring what the concept of similarity means to humans and how it can be implemented 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.)

Module 8

• Building models of customer behaviors 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

Module 9

• Analyzing your organization, its people, and environment as a network of inter-relationships
• Visualizing these relationships to uncover previously unseen business insights
• Exploring ego-centric and socio-centric methods of analyzing connections important to your organization

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