Booking options
Price on Enquiry
Price on Enquiry
Delivered Online
3 days
All levels
Duration
3 Days
18 CPD hours
This course is intended for
This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer.
Overview
Working in a hands-on learning environment, guided by our expert team, attendees will learn to
Understand the main concepts and principles of predictive analytics
Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations
Learn to deploy a predictive model's results as an interactive application
Learn about the stages involved in producing complete predictive analytics solutions
Understand how to define a problem, propose a solution, and prepare a dataset
Use visualizations to explore relationships and gain insights into the dataset
Learn to build regression and classification models using scikit-learn
Use Keras to build powerful neural network models that produce accurate predictions
Learn to serve a model's predictions as a web application
Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.
The Predictive Analytics Process
Technical requirements
What is predictive analytics?
Reviewing important concepts of predictive analytics
The predictive analytics process
A quick tour of Python's data science stack
Problem Understanding and Data Preparation
Technical requirements
Understanding the business problem and proposing a solution
Practical project ? diamond prices
Practical project ? credit card default
Dataset Understanding ? Exploratory Data Analysis
Technical requirements
What is EDA?
Univariate EDA
Bivariate EDA
Introduction to graphical multivariate EDA
Predicting Numerical Values with Machine Learning
Technical requirements
Introduction to ML
Practical considerations before modeling
MLR
Lasso regression
KNN
Training versus testing error
Predicting Categories with Machine Learning
Technical requirements
Classification tasks
Credit card default dataset
Logistic regression
Classification trees
Random forests
Training versus testing error
Multiclass classification
Naive Bayes classifiers
Introducing Neural Nets for Predictive Analytics
Technical requirements
Introducing neural network models
Introducing TensorFlow and Keras
Regressing with neural networks
Classification with neural networks
The dark art of training neural networks
Model Evaluation
Technical requirements
Evaluation of regression models
Evaluation for classification models
The k-fold cross-validation
Model Tuning and Improving Performance
Technical requirements
Hyperparameter tuning
Improving performance
Implementing a Model with Dash
Technical requirements
Model communication and/or deployment phase
Introducing Dash
Implementing a predictive model as a web application
Additional course details:
Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.
This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.
Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.
While we feel this is the best course for the Hands-on Predicitive Analytics with Python (TTPS4879) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.
Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.
Nexus Human, established over 20 years ago, stands as a pillar of excellence in the realm of IT and Business Skills Training and education in Ireland and the UK....