Description

Course Description:

The ML and AI for Predictive Analytics course is designed to equip learners with the skills to build intelligent systems that forecast trends, identify patterns, and support data-driven decision-making. This program covers core concepts of machine learning, supervised and unsupervised models, feature engineering, statistical techniques, and advanced AI algorithms used in real-world predictive scenarios. Students will gain hands-on experience using tools like Python, Scikit-learn, TensorFlow, and cloud-based analytics platforms to develop end-to-end predictive solutions. By the end of the course, learners will be able to apply predictive modeling to business, finance, marketing, operations, and emerging AI applications with confidence and industry relevance.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After ML and AI for Predictive Analytics Certified Course:

• Machine Learning Engineer
• Data Scientist
• Predictive Analytics Specialist
• AI Engineer
• Business Intelligence Analyst
• Data Analyst / Senior Data Analyst
• Big Data Engineer
• Decision Science Analyst
• Risk Modeling & Forecasting Analyst
• Marketing & Customer Insights Analyst

Essential Skills you will Develop ML and AI for Predictive Analytics Certified Course:

• Machine Learning Model Development
• Data Preprocessing & Feature Engineering
• Predictive Modeling & Forecasting
• Statistical Analysis & Hypothesis Testing
• AI & Deep Learning Implementation
• Data Visualization & Reporting
• Python & ML Libraries (Scikit-learn, TensorFlow, etc.)
• Cloud-Based Analytics & Deployment
• Problem-Solving with Data-Driven Insights
• Business Decision Support Using Predictive Analytics

Tools Covered:

• Python
• R Programming
• Scikit-learn
• TensorFlow / Keras
• Pandas & NumPy
• Matplotlib & Seaborn
• Jupyter Notebook
• Power BI / Tableau
• (GCP) / AWS ML Services
• SQL & BigQuery

Syllabus:

Module 1: Introduction to Predictive Analytics & AI Overview of predictive analytics, AI, and ML Applications across industries.

Module 2: Data Collection and Preprocessing Data cleaning, transformation, normalization Handling missing values and outliers.

Module 3: Exploratory Data Analysis (EDA) Data visualization techniques Statistical analysis and correlation.

Module 4: Supervised Learning Algorithms Regression (Linear, Logistic) Decision Trees, Random Forests, and Gradient Boosting.

Module 5: Unsupervised Learning Algorithms Clustering (K-Means, Hierarchical) Dimensionality Reduction (PCA, t-SNE).

Module 6: Time Series Forecasting ARIMA, SARIMA, and Prophet models Trend and seasonality analysis.

Module 7: Deep Learning for Predictive Analytics Neural networks basics Implementing AI models using TensorFlow/Keras.

Module 8: Model Evaluation & Optimization Cross-validation, hyperparameter tuning Metrics: RMSE, MAE, accuracy, F1-score.

Module 9: Deployment & Cloud-Based Predictive Analytics Deploying models on cloud platforms (AWS/GCP) nReal-time prediction pipelines.

Module 10: Industry Projects & Case Studies Hands-on projects in finance, marketing, healthcare End-to-end predictive analytics solution building.

Industry Projects:

• Sales Forecasting
• Customer Churn Prediction
• Financial Risk Analysis
• Inventory Demand Prediction
• Marketing Campaign Analytics
• Healthcare Predictive Analytics
• Stock Market Trend Prediction
• Energy Consumption Forecasting
• Fraud Detection System
• Sentiment Analysis

Who is this program for?

• Aspiring Data Scientists
• Business Analysts
• Machine Learning Enthusiasts
• IT Professionals & Developers
• Finance, Marketing
• Students & Graduates
• Project Managers
• Entrepreneurs & Startups
• Professionals transitioning to AI
• Data Science roles.
• Anyone passionate

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com