Predictive Analytics training course

Course Details

Warm welcome to the “Predictive Analytics” course offered by LPC. In a data-driven world, predictive analytics stands as an important tool to gain valuable insights, make informed decisions, and stay ahead of the competition. LPC designed this comprehensive course to equip you with essential knowledge and tools to excel in the field in predictive analytics.

Over the next 5 days, you’ll explore the fascinating realm of predictive analytics, from understanding the fundamental concepts of predictive analytics to mastering its techniques, even the advanced ones. Through a combination of theoretical learning and hands-on practical exercises, you’ll dive into various aspects of predictive analytics, including data preparation, supervised and unsupervised learning techniques, advanced analytics methods, deep learning, and model selection and optimization.

By the end of the course, you’ll be able to effectively apply the skills and knowledge learnt in real-world scenarios. And you’ll be confidence to tackle complex data analytics challenge with ease. Join us at LPC and embark on a transformative learning journey that will unlock exciting opportunities in the field of predictive analytics.

By the end of this course, participants should be able to:

Understanding predictive analytics fundamentals, including key terms, types of predictive analytics and its benefits
Determine the role of machine learning in predictive analytics
Master supervised learning techniques, including regression and classification algorithms
Apply unsupervised learning techniques, including clustering and dimensionality reduction methods
Implement advanced analytics techniques, including ensemble learning and deep learning
Understand the fundamentals of deep learning and its application
Select the best model after evaluation
Optimize model performance and avoid bias

Data Analysts
Software Engineers
Data Scientists
Business Analysts
Software Developers
Marketers
Sales Professionals
IT Professionals
Managers
Executives
Business-Owners
Anyone interested in data science

Course Outline

5 days course

Day 1

Day 2

Day 3

Day 4

Day 5

Introduction to Predictive Analytics

Overview of data analytics and its types:

Descriptive
Diagnostic
Prescriptive
Predictive

Understanding predictive analytics and its key concepts
The benefits of predictive analytics
Role of machine learning in predictive analysis
Preparing data for analysis

Supervised Learning Techniques

Defining supervised learning
Types of supervised learning:

Regression analysis
Classification problems

Understanding Regression Analysis algorithms:

Linear regression
Polynomial regression
Logistic regression

Understanding Classification problems:

Decision tree and random forests algorithms
Support vector machine (SUM) algorithm

Metrics to evaluate regression and classification models

Unsupervised Learning

Defining unsupervised learning
Types of unsupervised learning

Clustering algorithm
Dimensionality reduction algorithm

Understanding Clustering algorithm:

K- means clustering algorithm
Density-based clustering algorithm
Hierarchical clustering algorithm

Understanding Dimensionality reduction and its application

Principles component analysis (PCA)
Singular value decomposition (SVD)
t-SNE

Metrics to evaluate unsupervised leaning techniques

Advanced Analytics Techniques

Identifying ensemble learning methods:

Bagging
Random forests
Boosting algorithms
Stacking & voting classifiers

Utilizing XGBoost and LightGBM for gradient boosting
Defining deep learning concepts and the role of artificial neural networks
Exploring deep learning framework:

TensorFlow
Keras
PyTorch

Building deep learning:

Convolutional neural networks (CNN)
Recurrent neural networks (RNN)

Model Selection & Optimization

Determining the importance of selecting the best model
Cross-validation technique to select the best model
Exploring model bias-variance trade-off

Overfitting
Underfitting

Course recap and Q&A
Final project presentation

Predictive Analytics

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