AI Predictive Analytics Online Course

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25 hrs of E-Learning Videos
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AI Predictive Analytics Course Overview

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AI Predictive Analytics Upcoming Batches

24-08-2026
Mon-FriWeekdays Regular
08:00 AM & 10:00 AM Batches(Class 1Hr - 2Hrs) / Per Session
26-08-2026
Mon - FriWeekdays Regular
06:00 PM & 08:00 PM Batches(Class 1Hr - 2Hrs) / Per Session
21-08-2026
Sat-SunWeekend Batch
09:00 AM & 01:00 PM Batches(Class 2Hr - 4Hrs) / Per Session
Can't find a batch? Pick your own schedule

AI Predictive Analytics Course Syllabus

The AI Predictive Analytics Course is designed to help learners understand how Artificial Intelligence, Machine Learning, statistics, and data analytics can be used to predict future trends and business outcomes. The course covers the complete predictive analytics lifecycle, from data collection and preprocessing to model development, evaluation, visualization, and deployment.

  • Introduction to Artificial Intelligence and Predictive Analytics
  • Predictive Analytics vs. Descriptive and Diagnostic Analytics
  • Predictive Analytics Lifecycle
  • Role of AI and Machine Learning in Predictive Analytics
  • Real-world applications and use cases
  • Understanding structured and unstructured data
  • Data collection and data sources
  • Data types and data quality
  • Exploratory Data Analysis (EDA)
  • Data visualization fundamentals
  • Identifying trends, patterns, and relationships
  • Data cleaning and transformation
  • Handling missing values
  • Outlier detection and treatment
  • Encoding categorical variables
  • Feature scaling and normalization
  • Feature selection and feature engineering
  • Descriptive statistics
  • Probability concepts
  • Correlation and covariance
  • Hypothesis testing
  • Statistical significance
  • Regression analysis fundamentals
  • Python fundamentals for analytics
  • NumPy and Pandas
  • Data manipulation and analysis
  • Matplotlib and Seaborn
  • Working with datasets
  • Building analytical workflows using Python
  • Simple and Multiple Linear Regression
  • Polynomial Regression
  • Logistic Regression
  • Model training and prediction
  • Performance evaluation
  • Practical regression use cases
  • Supervised and unsupervised learning
  • Decision Trees
  • Random Forest
  • Support Vector Machines
  • K-Nearest Neighbors
  • Naive Bayes
  • Ensemble learning techniques
  • Gradient Boosting
  • XGBoost concepts
  • Model optimization
  • Hyperparameter tuning
  • Cross-validation
  • Feature importance
  • Model comparison
  • Introduction to time-series data
  • Trend and seasonality
  • Moving averages
  • ARIMA fundamentals
  • Forecasting techniques
  • Demand and sales forecasting
  • Time-series model evaluation
  • AI-driven predictive modeling
  • Automated machine learning concepts
  • Predictive recommendation systems
  • Anomaly and risk detection
  • AI-powered decision support
  • Generative AI applications in analytics
  • Training, validation, and testing datasets
  • Accuracy, Precision, Recall, and F1 Score
  • MAE, MSE, RMSE, and R²
  • Confusion Matrix and ROC-AUC
  • Overfitting and underfitting
  • Model optimization techniques
  • Building predictive dashboards
  • Interactive data visualization
  • Power BI/Tableau integration concepts
  • KPI and business metric analysis
  • Presenting predictive insights
  • Data-driven decision-making
  • Introduction to model deployment
  • Saving and loading machine learning models
  • API concepts for predictive models
  • Cloud-based analytics concepts
  • Monitoring predictive models
  • Model maintenance and updates
  • Sales and revenue forecasting
  • Customer churn prediction
  • Fraud detection
  • Financial risk prediction
  • Healthcare analytics
  • Marketing analytics
  • Supply chain and demand forecasting
  • Predictive maintenance
  • End-to-end predictive analytics workflow
  • Data preparation and exploratory analysis
  • Feature engineering
  • Predictive model development
  • Model evaluation and optimization
  • Dashboard and insight creation
  • Project presentation and business recommendations
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AI Predictive Analytics Hands on Projects

The AI Predictive Analytics Hands-On Projects provide practical experience in using Artificial Intelligence, Machine Learning, and data analytics techniques to solve real-world business problems. Learners work with real-world datasets to perform data preprocessing, exploratory analysis, predictive modeling, model evaluation, and visualization.

Customer Churn Prediction Project

Build an AI-powered predictive model to identify customers who are likely to leave a business. The project covers data preprocessing, feature engineering, classification algorithms, model evaluation, and prediction of high-risk customers.

Sales & Demand Forecasting Project

Develop a predictive analytics solution to forecast future sales and product demand using historical data. Learners analyze trends and seasonality, build forecasting models, evaluate predictions, and generate insights to support inventory and business planning.

Fraud Detection & Risk Prediction Project

Create a machine learning model to detect potentially fraudulent transactions and identify high-risk activities. The project includes data analysis, anomaly detection, classification techniques, model evaluation, and visualization of fraud-related insights.

For Corporates

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Our Instructor
Name
Mr. Ganesh
Experience
15+ Years
Specialized in
Artificial Intelligence, Machine Learning, Data Analytics, Predictive Modeling.
More Details
Mr. Ganesh is an experienced AI and Predictive Analytics professional with 15+ years of industry experience. He specializes in Artificial Intelligence, Machine Learning, Data Analytics, Predictive Modeling, and real-world business applications. His practical, industry-focused approach helps learners develop strong technical skills through hands-on projects and real-world use cases.

AI Predictive Analytics Course Objectives

Our AI Predictive Analytics Course aims to develop practical skills in using Artificial Intelligence, Machine Learning, statistics, and data analytics to predict trends, identify patterns, and support data-driven business decisions.

To teach learners how to analyze historical data and build AI models that accurately predict future outcomes.

Learners develop skills in data preprocessing, feature engineering, predictive modeling, machine learning, visualization, evaluation, and business analytics.

Predictive analytics helps businesses forecast trends, identify risks, understand customer behavior, optimize operations, and make informed strategic decisions.

This course covers Python, Pandas, NumPy, machine learning libraries, visualization tools, statistical techniques, and predictive analytics frameworks.

Students complete real-world projects involving customer churn, sales forecasting, fraud detection, model development, evaluation, and business insights.

Job Assistance Program

Our Job Assistance Programme offers you special guidance through the course curriculum and helps in your interview preparation.

Specialised Curriculum
Get on-field knowledge and skills from our expert instructors.
Assessment
Upgrade your on-field skills with our assessments and track your progress in real time.
Hands-on Project
Our hands-on project help you gain experience in real-time working.
Certification Guidance
A global certificate always helps you stand out from the crowd.
Portfolio Building
Experts guide you to maximise your profile with current industry trends that employers expect.
Placment Cell
We promote your abilities and showcase your portfolio to employers.

AI Predictive Analytics Career Opportunity

AI Predictive Analytics offers strong career opportunities across finance, healthcare, e-commerce, marketing, manufacturing, and technology. Professionals can pursue roles such as Predictive Analyst, Data Scientist, Machine Learning Engineer, BI Analyst, and AI Consultant.

Annual Pay Scale
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Placement Guidance & Interview Preparation

Infibee’s placement guidance navigates you to your desired role in top organisations, ensuring you stand out and excel in every opportunity.

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I joined Infibee in order to take a Data Science Course. Being from a non-IT background, I believe that being an IT Professional will be difficult for me. But now I believe that joining Infibee is the best decision I've ever made. My overall experience has been excellent. The teaching and non-teaching staff are both excellent. I will never forget the experience I had with Infibee. Thank you for your help and support, Infibee.
Muthu krishnan
I graduated without an IT background, but Infibee has helped me advance my career as a data scientist. Here, mentors are very helpful. With the right guidance and dedication, you can achieve your dreams. Self-study is also crucial if you want to stand out from the crowd and seize your opportunities.Companies frequently visit Infibee for placements and take some incredible talent with them.
Pranali
I enrolled in Infibee's PG Data Science course. The training experience was excellent, with 80% practical training and 20% theory, which was extremely beneficial. I learned a great deal. My placement process began after I completed my course, and I am now working as an RPA and Data Science Intern at rsutra. Nisha Mam was extremely helpful during the placement process.
Yuvaraj
The courses on Infibee are excellent. It has great value. I was non IT person and joined for Data Science course it was really helpful and interesting learning with Infibee. Teachers are also incredible they did an excellent job of ensuring that we understood each concept. Excellent job setting up the mock test and interview. I enjoyed finding more skill out of me from Infibee.I appreciate Infibee's assistance in advancing my career.
Lavanya
I completed Full Stack Development Course at infibee. Infibee is the best training institute. My trainer taught us the best concepts out there. His teaching skills are great. They are having lots of knowledge. The way of teaching is also good. I am satisfied with the course. Glad to have found this institute.
Madhaiyan Madhan

AI Predictive Analytics Course FAQ's

Infibee AI Predictive Analytics Course offers wide range of services that suits for both fresher and experienced persons via both offline and online at your suitable time slots.

You need not worry about having missed a class. Our dedicated course coordinator will help them with anything and everything related to administration. The coordinator will arrange a session for the student with trainers in place of the missed one.

Yes, of course. You can contact our team at Infibee Technologies, and we will schedule a free demo or a conference call with our mentor for you.

We provide classroom, online, and self-based study material and recorded sessions for students based on their individual preferences.

Yes, all our trainers are industry professionals with extensive experience in their respective domains. They bring hands-on practical and real-world knowledge to the training sessions.

Yes, participants typically receive access to course materials, including recorded sessions, assignments, and additional resources, even after the training concludes.

We provide placement assistance to students, including resume building, interview preparation, and job placement support for a wide range of software courses.

Yes, we offer customisation of the syllabus for both individual candidates and corporate also.

Yes, we offer corporate training solutions. Companies can contact us for customised programmes tailored to their team’s needs.

Participants need a stable internet connection and a device (computer, laptop, or tablet) with the necessary software installed. Detailed technical requirements are provided upon enrollment.

In most cases, such requests can be accommodated. Participants can reach out to our support team to discuss their preferences and explore available options.

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