Predictive Analytics and Machine Learning Models Training
Predictive Analystics and Machine Learning Models Training is a 2-day course where participants learn core concepts of predictive analytics and machine learning as well as gain proficiency in data preprocessing, feature engineering, and data visualization.
Predictive analytics (PA) and machine learning (ML) are powerful tools for uncovering insights in large volumes of data.
Many organizations use machine learning for personalizing consumers’ website experiences and predictive analytics for forecasting outcomes of campaigns.
Benefits of predictive analytics and machine learning, include:
- Predictive analytics using machine learning algorithms can provide more accurate and precise predictions
- Automate decision-making processes
- Scale up to handle large datasets and complex problems
Both machine learning and predictive analytics are used to make predictions on a set of data about the future.
Predictive analytics uses predictive modelling, which can include machine learning. Predictive analytics has a very specific purpose: to use historical data to predict the likelihood of a future outcome.
At its most basic, analytics of any sort is simply applied mathematics—sometimes known as data science.
The audience of predictive analytics tends to be people, adding an extra level of necessary communication and interpretability to its work.
To Predictive Analysts, machine learning is an extension of their practice, another tool in their toolbox, that helps them to do their job better. Using ML, predictive analysts can better provide answers, with confidence, to more complex problems.
This combination also offers real-time answers to questions that persist through time with ever-changing data as well as helps explore entirely new kinds of problems.
Predictive Analytics and Machine Learning Models Training by Tonex
Predictive Analytics and Machine Learning Models Training is an intensive course designed to equip professionals with the essential skills and knowledge required to leverage predictive analytics and machine learning techniques in business and industry.
This comprehensive training program covers the fundamentals of predictive modeling, machine learning algorithms, and practical applications to drive data-driven decision-making.
Learning Objectives:
Upon completing this course, participants will:
- Understand the core concepts of predictive analytics and machine learning.
- Gain proficiency in data preprocessing, feature engineering, and data visualization.
- Master various machine learning algorithms and their applications.
- Develop the ability to build, evaluate, and optimize predictive models.
- Apply predictive analytics and machine learning to real-world business problems.
- Enhance their career prospects by acquiring valuable data science skills.
Audience:
This course is ideal for:
- Data Scientists and Analysts seeking to enhance their predictive modeling skills.
- Business Analysts and Managers interested in leveraging data for decision-making.
- IT professionals looking to transition into data science and machine learning roles.
- Researchers and academics exploring the applications of machine learning.
- Entrepreneurs and business leaders interested in data-driven strategies.
- Anyone looking to upskill and excel in the field of predictive analytics and machine learning.
Course Outline:
Introduction to Predictive Analytics and Machine Learning
- Overview of predictive analytics and machine learning
- Importance and impact in various industries
- Key terminology and concepts
- Data preparation and cleaning
- Tools and platforms for machine learning
- Ethical considerations in machine learning
Data Preprocessing and Feature Engineering
- Data collection and storage
- Data cleaning and missing value handling
- Data transformation and scaling
- Feature selection and extraction
- Handling categorical data
- Dealing with imbalanced datasets
Supervised Learning Algorithms
- Linear regression
- Logistic regression
- Decision trees and random forests
- Support vector machines
- k-Nearest Neighbors (k-NN)
- Naïve Bayes classification
Unsupervised Learning Algorithms
- Clustering techniques (k-means, hierarchical)
- Dimensionality reduction (PCA, LDA)
- Association rule mining (Apriori, FP-growth)
- Anomaly detection methods
- Recommender systems
- Case studies in unsupervised learning
Model Evaluation and Hyperparameter Tuning
- Cross-validation techniques
- Evaluation metrics (accuracy, precision, recall, F1-score)
- Hyperparameter tuning and optimization
- Bias-variance trade-off
- Model selection and ensemble methods
- Real-world model evaluation challenges
Applying Predictive Analytics and Machine Learning
- Use cases and practical applications in different industries
- Building a predictive model from scratch
- Deploying and monitoring machine learning models
- Ethical considerations and bias in model predictions
- Communicating results to stakeholders
- Future trends and advanced topics in machine learning
This comprehensive training program will empower participants to harness the potential of predictive analytics and machine learning to make informed decisions and solve complex problems across various domains.