The Certified Machine Learning Associate course provides a comprehensive introduction to the fundamental concepts and techniques of machine learning. Designed for beginners and those with limited prior knowledge, this course aims to equip participants with a solid foundation in this rapidly evolving field. Throughout the program, participants will gain a deep understanding of key machine learning concepts, including data preprocessing, model selection, training, and evaluation. They will learn to implement various algorithms for tasks such as classification, regression, and clustering. Practical hands-on exercises and projects will be a central component, allowing participants to apply their knowledge to real-world scenarios.

Additionally, the course covers essential topics like feature engineering, model interpretation, and ethical considerations in machine learning. Participants will also explore popular machine learning libraries and frameworks, enabling them to build and deploy models efficiently.


By the end of the course, participants will have the skills and knowledge to confidently engage in basic machine learning projects. Whether pursuing a career in data science, artificial intelligence, or seeking to enhance their analytical capabilities, this certification provides a strong foundation for further studies and professional development in the field of machine learning.


USD 29,700 /-
USD 35,200 /-

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Course Highlights

Introduction to Machine Learning: Basics of ML, its applications, and its importance in various industries.

Mathematical Foundations: Fundamentals of linear algebra, calculus, and probability required for ML.

Data Preprocessing: Techniques for data cleaning, normalization, and handling missing values.

Supervised Learning: Understanding classification and regression algorithms, such as linear regression, decision trees, and support vector machines.

Unsupervised Learning: Exploring clustering and dimensionality reduction techniques like k-means clustering and principal component analysis.

Model Evaluation: Methods for assessing model performance, including metrics like accuracy, precision, and recall.

Feature Engineering: Strategies for feature selection and extraction to improve model accuracy.

Deep Learning Basics: Introduction to neural networks, activation functions, and training neural networks.

Model Deployment: Techniques for deploying ML models in real-world applications.

Ethical Considerations: Discussions on the ethical implications of ML and bias mitigation.

Practical Projects: Hands-on experience with ML tools and libraries like Python, sci-kit-learn, and TensorFlow.


After you complete the course with Skillfloor, course certification, offer a comprehensive introduction to machine learning using TensorFlow, a popular open-source machine learning framework. This program equips learners with essential skills in building and deploying machine learning models, enabling them to work on real-world projects and enhance their career prospects in the field of artificial intelligence and machine learning. This certification serves as a testament to your foundational knowledge and skills in machine learning concepts, algorithms, and techniques. It validates your ability to preprocess data, build predictive models, and evaluate their performance. With this certification, you showcase your readiness to contribute to machine learning projects and demonstrate your commitment to staying at the forefront of emerging technologies. As a Certified Machine Learning Associate, you not only gain recognition for your expertise but also open doors to entry-level positions in data science, artificial intelligence, and related fields, where you can leverage your skills to make meaningful contributions to data-driven decision-making and innovation.



Top 10 Reasons For Choosing this Course

Cutting-Edge Technology: Learn the latest advancements in machine learning with TensorFlow, one of the most widely used frameworks in the field.

Hands-On Experience: Gain practical experience by working on real-world projects, allowing you to apply what you learn.

Industry-Relevant Curriculum: Stay up-to-date with industry demands and trends through a curriculum designed to meet the needs of the job market.

Expert Instructors: Benefit from expert guidance and insights from experienced instructors with extensive knowledge in machine learning.

Certification: Obtain a valuable certification recognized by Skillfloor and IABAC, enhancing your resume and career prospects.

Flexibility: Choose from flexible learning options, including online and in-person classes, to fit your schedule and learning preferences.

Community: Join a vibrant learning community of like-minded individuals, fostering collaboration and networking opportunities.

Career Advancement: Open doors to exciting career opportunities in artificial intelligence, data science, and machine learning.

Practical Applications: Acquire skills that can be applied across various industries, including healthcare, finance, and technology.

Future-Proofing: Position yourself for a future-proof career in an ever-evolving field, as machine learning continues to shape industries worldwide.


Course Curriculum

  •  Understanding the basics of Machine Learning
     Different types of Machine Learning
     Machine Learning pipeline
     Machine Learning landscape

  •  Data cleaning and transformation
     Data visualization
     Handling missing data
     Feature scaling

  •  Linear Regression
     Logistic Regression
     Decision Trees
     Random Forests
     Support Vector Machines
     Naive Bayes
     KNearest Neighbors

  •  Clustering
     Hierarchical Clustering
     Principal Component Analysis
     Dimensionality Reduction

  •  Overfitting, Underfitting and Regularization
     Model Selection
     Model evaluation using crossvalidation
     Performance Metrics

  •  Artificial Neural Networks
     Convolutional Neural Networks
     Recurrent Neural Networks
     Generative Adversarial Networks
     Reinforcement Learning

  •  Image Classification
     Natural Language Processing
     Recommender Systems
     Fraud Detection
     Churn Prediction
     Sentiment Analysis

  •  Setting up the environment for Machine Learning
     Accessing data for Machine Learning
     Implementing Machine Learning algorithms
     Debugging Machine Learning algorithms

  •  Bias in Machine Learning
     Fairness in Machine Learning
     Privacy in Machine Learning
     Transparency in Machine Learning

  •  Review of all the topics covered in the course
     Practice exams and quizzes
     Tips and tricks to excel in the exam
     Certification process and requirements

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