Intermediate Machine Learning
Intermediate Machine Learning Course Online by Checkmate IT Tech offer a transformative journey, elevating your expertise and mastering essential skills. Position yourself for success in the dynamic field of Machine Learning by enrolling today. Unlock new career opportunities!
- 10+ Courses
- 30+ Projects
- 400 Hours
Intermediate Machine Learning Training is suitable for the following target audiences:
Data Scientists and Analysts: Data scientists and analysts are experts who wish to learn more about machine learning algorithms and use cutting-edge methods to solve practical issues.
Software developers: Those who want to improve their programming skills by adding machine learning features to apps.
AI Enthusiasts: AI enthusiasts have a foundational understanding of machine learning and a strong desire to learn more to address increasingly challenging AI problems.
Students and Academicians: People who are interested in working in the fields of machine learning, data science, or artificial intelligence.
Business analysts: These analysts want to use machine learning to make data-driven decisions, plan strategically, and do predictive modeling.
Machine Learning Engineer: Create and implement machine learning models to enhance goods and services in the banking, healthcare, and technology sectors.
Data Scientist: Examine huge datasets using machine learning to find trends and offer useful insights.
AI Research Scientist: Develop novel solutions by working on state-of-the-art machine learning and artificial intelligence research.
Business Intelligence Developer: To improve reporting and analytics capabilities, including machine learning models into BI platforms.
Software Engineer emphasizing ML/AI: Create software programs that use machine learning techniques to provide more intelligent solutions.
Product Manager (AI/ML): Ensure AI-powered solutions are developed and implemented under company objectives.
AI Consultant: Offer companies guidance on incorporating machine learning solutions to address operational issues and spur expansion.
With high-paying employment prospects and room for advancement in the USA and Canada, machine learning talents are highly sought in various industries, including technology, healthcare, finance, e-commerce, and more.
- Recap of machine learning concepts and workflows
- Supervised vs. unsupervised learning review
- Data preparation and feature engineering basics
- Understanding model bias and variance
- Introduction to Scikit-learn workflows
- Hands-on: Preparing and training a basic ML model
- Advanced data cleaning techniques
- Handling missing values and categorical data
- Feature scaling and normalization methods
- Feature selection strategies
- Dimensionality reduction overview
- Hands-on: Building a preprocessing pipeline
- Linear regression review and limitations
- Polynomial regression models
- Ridge and Lasso regression techniques
- Evaluating regression models using performance metrics
- Hands-on: Predictive modeling using regression algorithm
- Logistic regression for classification
- Decision trees and model interpretability
- K-Nearest Neighbors (KNN) algorithm
- Model comparison and evaluation
- Practice: Building and evaluating classification models
- Introduction to ensemble learning
- Random Forest algorithms
- Gradient Boosting techniques
- Model improvement using ensemble methods
- Clustering algorithms overview
- K-Means clustering in detail
- Hierarchical clustering techniques
- Introduction to anomaly detection
- Hands-on: Identifying patterns using clustering models
- Cross-validation methods
- Confusion matrix and classification metrics
- Precision, recall, and F1 score
- Hyperparameter tuning with grid search
- Hands-on: Optimizing machine learning models
- Working with real-world datasets
- Introduction to model deployment concepts
- Final project: Build and evaluate a machine learning model
- Mock up interviews
This course is designed for learners who already understand basic machine learning concepts and want to build deeper practical skills.
Yes. Participants should already understand basic ML concepts such as regression, classification, and data preprocessing.
The duration is 2 months (8 weeks), with sessions held 2 times per week (either during week or weekends), including theory, hands-on practice and project work.
Yes, upon successful completion, you’ll receive a Certificate of Completion from Checkmate IT Tech.
Yes. Every module includes practical labs and assignments.
We offer online training classes to promote easy access to all candidates. Recordings are also made available for revision or if you miss a session.
Yes. We provide resume reviews, mock interviews, LinkedIn optimization, and guidance on job portals to help boost your chances in the job market.
Students will work with regression, classification, clustering, and ensemble models.
Students typically work with libraries such as NumPy, Pandas, Scikit-learn and Matplotlib.
You can enroll via our website or contact our support team directly via email or phone. We’ll guide you through the quick and easy registration process.
Email info@checkmateittech.com OR Call Us +1-347-4082054
- Submit Form
Job opportunities in USA and Canada
Machine Learning Engineer: Create and implement machine learning models to enhance goods and services in the banking, healthcare, and technology sectors.
Data Scientist: Examine huge datasets using machine learning to find trends and offer useful insights.
AI Research Scientist: Develop novel solutions by working on state-of-the-art machine learning and artificial intelligence research.
Business Intelligence Developer: To improve reporting and analytics capabilities, including machine learning models into BI platforms.
Software Engineer emphasizing ML/AI: Create software programs that use machine learning techniques to provide more intelligent solutions.
Product Manager (AI/ML): Ensure AI-powered solutions are developed and implemented under company objectives.
AI Consultant: Offer companies guidance on incorporating machine learning solutions to address operational issues and spur expansion.
With high-paying employment prospects and room for advancement in the USA and Canada, machine learning talents are highly sought in various industries, including technology, healthcare, finance, e-commerce, and more.
