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Introduction to Data Science, Machine Learning & AI using Python

Introduction to Data Science, Machine Learning & AI using Python

Course 1264

Duration: 5 days
Language: English
Level: Foundation

Get This Course £2,395

5-day instructor-led training course
After-course coaching available
After-course computing sandbox access included
End-of-course exam included

Include dates with afternoon start times

#1264

Guaranteed to Run - you can rest assured that the class will not be cancelled.
Jun 6 - 10 2:00 PM - 9:30 PM BST

AnyWare
Jun 13 - 17 9:00 AM - 4:30 PM BST

AnyWare
Sep 19 - 23 2:00 PM - 9:30 PM BST

New York or AnyWare
Oct 10 - 14 9:00 AM - 4:30 PM BST

AnyWare
Oct 31 - Nov 4 1:00 PM - 8:30 PM GMT

Herndon, VA or AnyWare
Dec 5 - 9 9:00 AM - 4:30 PM GMT

London or AnyWare
Mar 6 - 10 9:00 AM - 4:30 PM GMT

AnyWare

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

#70I8

Aug 31 10:00 AM - 5:30 PM BST

AnyWare
Nov 21 9:00 AM - 4:30 PM GMT

AnyWare
Jan 30 9:00 AM - 4:30 PM GMT

AnyWare
May 8 10:00 AM - 5:30 PM BST

AnyWare

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

#1264

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 visualise raw data to create predictive models that will help you find and evaluate future opportunities.

Data Science Training in Python Delivery Methods

In-Person
Online

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, 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 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 organisation
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, Standardising and Normalising Data
Visualising 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

Analysing your organisation, its people, and 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 important to your organisation

Module 10

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

Unlimited Access Data Science in Python Premium Blended Training

Premium Blended Training Benefits

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, Introduction to Python for Data Analytics.

eBooks (Bundle Only)

Managing Data Science
Hands-On Data Analysis with Pandas
Hands-On Python Natural Language Processing
Data Science Algorithms in a Week - Second Edition
Machine Learning for Algorithmic Trading - Second Edition
Hands-On Neural Networks
Mastering Machine Learning Algorithms - Second Edition
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
Hands-On Data Science for Marketing
Python Feature Engineering Cookbook
Artificial Intelligence By Example - Second Edition
Hands-On Machine Learning on Google Cloud Platform

On-Demand Videos (Bundle Only)

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

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Data Science Training in Python Course FAQs

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 model and analyses issues throughout the period of the training.

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

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.

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

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

Once you are enroled in the programme, specific details and dates will be sent to you.

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 enroling in the event date of your choice.