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Dubai	November 2, 2025	November 6, 2025	5

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Goals

Understanding the basics of machine learning and predictive analytics.

Learn about the types of predictive models and their uses.

Using Python and R in machine learning applications.

Analyzing historical data and building models to predict the future.

Application of classification and regression algorithms in predictions.

Evaluate and improve the performance of predictive models.

Dealing with big data using machine learning.

Developing predictive dashboards to assist decision makers.

Introduction to Predictive Analytics and Machine Learning

The concept and importance of predictive analytics.

The difference between machine learning and deep learning.

The use of artificial intelligence in predictive analytics.

Applications of predictive analytics in various sectors.

Data Collection and Analysis

Data sources used in forecasting.

Data cleaning and preparation for predictive models.

Big data handling techniques.

Using Pandas and NumPy in data processing.

Fundamentals of Machine Learning and Predictions

Introduction to predictive algorithms.

The difference between supervised and unsupervised learning.

Using Scikit-learn to build predictive models.

Evaluating predictive models using Metrics.

Prediction and Regression Algorithms

Linear Regression Analysis.

Using Logistic Regression to Predict Classifications.

Time Series Forecasting Using ARIMA and LSTM.

Improving Model Performance Using Hyperparameter Tuning.

Time-lapse data analysis and forecasting

The concept of time series analysis.

Statistical modeling methods for predicting trends.

Using Facebook Prophet in analyzing time series data.

Classification and Clustering in Machine Learning

Classification algorithms such as Decision Trees and Random Forest.

Using SVM (Support Vector Machines) in predictions.

Clustering techniques using K-Means Clustering.

Pattern analysis of unclassified data.

Improving Predictive Models

The concept of Overfitting and Underfitting.

Methods for improving model performance using Cross Validation.

Selecting important features using Feature Selection.

Implementing Grid Search and Random Search to improve models.

Natural Language Processing (NLP) in Predictions

Text analysis using machine learning.

Chatbots and smart assistants applications in business.

Using Sentiment Analysis to analyze customer sentiment.

Building predictive models using text data.

Certificate

Upon full attendance of this training course, participants will receive an accredited certificate issued by Al Manhal Institute, which is licensed by the Abu Dhabi Center for Technical and Vocational Education and Training. This certificate is regionally recognized, providing holders with a competitive advantage both within their organizations and when seeking new career opportunities.

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Predictive Analysis Using Machine Learning

Schedule

Goals

Contents

Introduction to Predictive Analytics and Machine Learning

Data Collection and Analysis

Fundamentals of Machine Learning and Predictions

Prediction and Regression Algorithms

Time-lapse data analysis and forecasting

Classification and Clustering in Machine Learning

Improving Predictive Models

Natural Language Processing (NLP) in Predictions

Certificate

Certificate

Extra Options

Introduction to Predictive Analytics and Machine Learning

Data Collection and Analysis

Fundamentals of Machine Learning and Predictions

Prediction and Regression Algorithms

Time-lapse data analysis and forecasting

Classification and Clustering in Machine Learning

Improving Predictive Models

Natural Language Processing (NLP) in Predictions