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machine learning and predictive models certification

Learn from industry-certified trainers with real-world experience in machine learning and predictive analytics.

Practical, project-based approach to build and refine predictive models using real-world data.

Access to advanced cloud labs and the latest machine learning software, ensuring a future-ready skillset.

24/7 access to learning resources and personalized guidance from Vinsys' dedicated support team.

Overview Learning Objectives Who Should Attend Prerequisite Outline

Overview

The Machine Learning and Predictive Models course by Vinsys provides a good starting point in the world of data science. This is an entry level course,which means there is no prerequisite to join this course as such. Across this course, you will discover how to leverage the data with the help of machine learning algorithms. You will go through the whole process from data cleaning to data preparation and from creating and optimizing predictive models.
This course will guide you through a number of machine learning algorithms and give an overview of how they work and how one can use them with actual data sets. You will also learn about model evaluation and selection, so that you are able to make your predictions with accuracy.
This course provides real-life project-based learning, and real-life practical activities that help you to apply machine learning in your workplace. By the end of the course, you will be equipped with all the knowledge and tools that will enable you to make right decisions as well as create significant models. Besides, on the successful completion of the course, you will receive internationally acknowledged certification certification from Vinsys.

Start your journey into the future of data science today!

Objectives

The course aims to equip learners with essential skills in data science and machine learning. By the end of the course, you will be able to:

Understand the definitions of and the differences between machine learning and predictive analytics.
Apply data cleaning and preparation techniques to real-world datasets.
Build and train predictive models based on the different machine learning techniques.
Evaluate model performance and increase accuracy with various measurements.
Optimize models for a better prediction and to make more accurate data driven decisions.
Develop applied training through assignments and exercises in machine learning.

This course provides a strong foundation for anyone looking to advance in the field of data science.

Audience

This program is designed for individuals who want to develop skills in data science and machine learning. It is ideal for:

Beginners with no prior experience in machine learning or data science.
Data analysts and business professionals looking to enhance their predictive modeling skills.
IT and software professionals interested in applying machine learning in real-world applications.
Students pursuing a career in data science, AI, or related fields.
Managers and decision-makers who want to leverage data-driven insights for business growth.
Anyone curious about learning how to use machine learning for problem-solving and innovation.
This course provides the perfect starting point for anyone keen on mastering machine learning and predictive analytics.

Eligibility Criteria

The Machine Learning and Predictive Models course by Vinsys welcomes learners from all backgrounds, with no prior experience required. Basic knowledge of statistics, such as averages, histograms, and standard deviation, will be helpful but is not mandatory. This course is ideal for beginners, data enthusiasts, IT professionals, and business analysts eager to learn how to apply machine learning and predictive modeling techniques to real-world problems.

Course Outline

Module 1: Introduction to Machine Learning and Predictive Modeling

Overview of machine learning concepts
Types of machine learning: supervised, unsupervised, and reinforcement learning
Introduction to predictive analytics and its applications
Understanding the machine learning workflow

Module 2: Data Collection, Cleaning, and Feature Engineering

Data collection techniques and data types (structured, semi-structured, unstructured)
Data preprocessing and cleaning (handling missing values, outliers, and inconsistencies)
Feature engineering: creating meaningful features from raw data
Dimensionality reduction and feature selection techniques

Module 3: Building Predictive Models

Understanding predictive models: classification and regression
Choosing the right predictive model based on data and goals
Implementing machine learning algorithms: decision trees, regression (linear & logistic), and support vector machines (SVM)
Model training and testing

Module 4: Advanced Machine Learning Algorithms

Introduction to neural networks and deep learning
Ensemble methods: Random Forest, Gradient Boosting, and XGBoost
Time-series analysis and predictive modeling
Clustering and anomaly detection (k-means, DBSCAN, etc.)

Module 5: Model Evaluation, Validation, and Optimization

Evaluating model performance: accuracy, precision, recall, F1 score, and ROC-AUC
Cross-validation techniques: k-fold, leave-one-out
Overfitting and underfitting issues and solutions
Model tuning and hyperparameter optimization (Grid Search, Random Search)

Module 6: Practical Applications of Predictive Models

Hands-on exercises: applying predictive models to real-world datasets
Case studies on predictive modeling in finance, healthcare, marketing, and e-commerce
Ethical considerations in machine learning and predictive analytics

Module 7: Deploying Machine Learning Models

Introduction to model deployment in production environments
Automation of model workflows and integration into business systems
Continuous monitoring and updating of deployed models

Module 8: Final Project and Capstone

End-to-end project: solving a business problem using machine learning and predictive models
Model development, evaluation, and presentation of findings
Feedback and assessment by course instructors

Choose Your Preferred Mode

Online Training

Choose convenient timings to fit your personal schedule.
Complete the course based on personal availability, with the freedom to revisit lessons as needed.
Engage in interactive live sessions with industry experts for real-time Q&A and discussions.
Unlimited access to course content, recorded sessions, and hands-on projects.

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Corporate Training

Customized learning modules aligned with the specific business needs and industry applications of the organization.
Incorporate company-specific data and scenarios to apply machine learning and predictive models directly to business challenges.
Promote collaborative problem-solving among teams through group exercises and projects.
Enable employees to earn certifications that contribute to both professional development and company expertise.

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FAQ’s

Why choose Vinsys for the Machine Learning and Predictive Models course?

Vinsys offers expert-led training with hands-on projects, flexible learning options, and real-world case studies, ensuring you gain practical machine learning skills applicable to industry needs.

What are the prerequisites for this course?

While no prior experience in machine learning is required, having basic knowledge of statistics (such as averages, histograms, and standard deviation) can be helpful.

What tools will I learn to use in this course?

You will gain proficiency in using industry-standard tools for machine learning and predictive modeling, including advanced cloud labs for practical applications.

How will this course help me in my career?

This course equips you with in-demand machine learning and predictive modeling skills, making you capable of handling data-driven projects and increasing your employability in data science roles.

What topics are covered in the Machine Learning and Predictive Models course?

Topics include data cleaning and preparation, building and training models, machine learning algorithms (such as decision trees, neural networks), model evaluation, and real-world applications.

Is the course suitable for beginners?

Yes, the course is designed for both beginners and professionals. No prior programming or machine learning experience is required, and the course takes a step-by-step approach.

Will I get practical experience during the course?

Absolutely. The course includes hands-on projects, real-world datasets, and practical exercises to apply machine learning concepts and build predictive models.

What kind of support will I receive during the course?

Vinsys provides ongoing support through expert trainers, 24/7 access to learning resources, and dedicated academic guidance to help you succeed throughout the course.

Why Vinsys

Seasoned Instructors

Official Vendor Partnerships

Authorized Courseware

3,000+ Courses & 2,000+ Modules

In Synch with Tech-advancements

Customizable Blended Learning Options

Reviews

This was a very useful course for beginners like me as it showed how to clean data and build models. The instructors were very clear in their explanation and I was able to follow the work through the practical projects done in the course. I feel quite comfortable with machine learning now.

Keran NagargoojeLeader - AI/ML/Generative AI | Advanced Machine Learning, Transformer Models

This course was flexible when it came to the modules and I could study at my own convenience. The case discussions used in the course were really useful in supporting issues like the machine learning algorithms in data science.

Manasi PatwardhanSenior Scientist, Deep Learning and AI Research Area

Trainers at Vinsys were very good in providing training in the field of predictive modeling when I worked there. I liked the examples that were given during the training I find this knowledge very useful especially in my field of business analytics.

Sankalp TomarSr. Data & Applied Scientist

In my opinion the course was a good introduction to machine learning starting from data pre-processing and ending with model evaluation practice. Altogether, the course is quite useful for anyone who decided to start learning machine learning from scratch.

Sangamesh KSData Science & PowerBI Freelance Consultant & Corporate Trainer

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