The Certified Machine Learning Expert course provides a comprehensive understanding of the principles and practices of machine learning. Participants will delve into fundamental concepts such as regression, classification, clustering, and deep learning. They will gain proficiency in various algorithms and techniques used to analyze and interpret data. The curriculum covers hands-on programming with languages like Python and R, along with popular ML libraries like TensorFlow and sci-kit learn. Participants will also learn to evaluate model performance, handle overfitting, and optimize hyperparameters. The course emphasizes real-world applications, showcasing how machine learning is employed in diverse industries, including finance, healthcare, and e-commerce. Additionally, ethical considerations and responsible AI practices are integrated into the curriculum, ensuring participants are equipped to navigate the ethical challenges associated with machine learning.

Throughout the program, participants engage in practical exercises, projects, and case studies to reinforce their understanding and develop practical skills. By the end of the course, participants will have the knowledge and expertise to design, implement, and deploy machine learning models effectively. Upon successful completion, participants receive certification as a Machine Learning Expert, validating their proficiency in this rapidly evolving field. This certification opens doors to a wide range of career opportunities in data s

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

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Course Highlights

Introduction to Machine Learning: Understanding the basics of machine learning, its applications, and its significance in various industries.

Mathematics for Machine Learning: Linear algebra, calculus, probability, and statistics, which form the mathematical foundation of machine learning algorithms.

Python Programming: In-depth Python programming for data manipulation, analysis, and implementation of machine learning models using libraries like NumPy, Pandas, matplotlib, and Scikit Learn.

Data Preprocessing and Exploration: Techniques for cleaning and preparing datasets, handling missing values, scaling features, and exploring data distributions.

Supervised Learning Algorithms: In-depth study of algorithms like Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, and K-nearest Neighbors.

Unsupervised Learning Algorithms: Clustering algorithms (KMeans, Hierarchical Clustering) and dimensionality reduction techniques (PCA, tSNE).

Model Evaluation and Selection: Techniques for assessing model performance, including metrics like accuracy, precision, recall, F1 Score, ROC AUC, and cross-validation.

Hyperparameter Tuning and Model Optimization: Methods for fine-tuning machine learning models to improve their performance.

Deep Learning and Neural Networks: Introduction to deep learning concepts, neural network architectures, and frameworks like TensorFlow and PyTorch.

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs): Specialized neural network architectures for image recognition and sequence data analysis, respectively.



After you complete the course with Skillfloor, you will receive a certification, offered in collaboration between Skillfloor and IABAC, which provides comprehensive training in machine learning techniques. This certification equips learners with the knowledge and skills needed to excel in the field of machine learning, covering topics such as data preprocessing, model building, and deployment. Upon successful completion, participants receive a prestigious certification that validates their expertise in machine learning, opening up exciting career opportunities in this rapidly evolving field.


Top 10 Reasons For Choosing this Course

Comprehensive Content: Look for a course that covers a wide range of machine learning topics, including fundamentals, advanced techniques, algorithms, and practical applications.

Experienced Instructors: Seek courses taught by experienced professionals or academics with a strong background in machine learning.

Hands-on Experience: Practical projects and labs are essential for applying what you've learned in real-world scenarios. Ensure the course includes such opportunities.

Real-World Applications: A good course should teach you how to apply machine learning techniques to real-world problems and datasets, enhancing your practical skills.

Engaging Learning: Engaging content, quizzes, and interactive elements can make the learning experience more effective and enjoyable.

Flexible Learning: Look for courses designed to accommodate different learning styles and schedules, allowing learners to progress at their own pace.

Certification: A recognized certification upon completion can add value to your resume and support career advancement. Verify that the course provides certification.

Community and Support: Access to a community of fellow learners and support from instructors or mentors can greatly enhance your learning experience and provide valuable insights.

Assessment and Feedback: Regular assessments and feedback help you identify areas where you need improvement, ensuring a more effective learning journey.

Additional Resources: Consider courses that offer access to additional materials, updates, or resources even after completing the course, allowing for ongoing learning and skill enhancement.

Course Curriculum

  •  Overview of machine learning and its applications
     Types of machine learning algorithms
     Fundamentals of statistical analysis and probability

  •  Data collection and cleaning techniques
     Feature selection and engineering methods
     Handling missing data and outliers

  •  Linear Regression
     Logistic Regression
     Decision Trees
     Support Vector Machines
     Naive Bayes Classifier

  •  KMeans Clustering
     Principal Component Analysis (PCA)
     Association Rule Mining

  •  Artificial Neural Networks (ANN)
     Convolutional Neural Networks (CNN)
     Recurrent Neural Networks (RNN)
     Deep Learning frameworks (Tensorflow, Keras, PyTorch)

  •  Model performance metrics (accuracy, precision, recall, F1 score, ROC curve)
     Bias and variance tradeoff
     Hyperparameter tuning techniques (Grid Search, Random Search, Bayesian Optimization)

  •  Developing endtoend machine learning pipelines
     Model serialization and deployment (Flask, Docker, Kubernetes)

  •  Reinforcement Learning
     Transfer Learning
     Natural Language Processing (NLP)
     Time Series Analysis

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