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Overview

Skillfloor offers a comprehensive machine learning course designed to equip learners with essential skills in AI and machine learning. This online machine-learning course covers fundamental concepts, algorithms, and techniques essential for building intelligent systems. Through practical hands-on exercises and real-world projects, participants gain proficiency in various machine-learning models and tools. Whether you're a beginner looking to dive into the world of machine learning or an experienced professional seeking to enhance your skills, this machine learning certification course provides a structured learning path to achieve your goals.


With Skillfloor's machine learning training, participants receive industry-recognized certification upon successful completion of the course. This machine learning certification course not only imparts theoretical knowledge but also focuses on practical applications, ensuring that learners can effectively apply machine learning techniques in diverse domains. Through interactive lectures, case studies, and assessments, students develop a deep understanding of machine learning concepts and gain hands-on experience with popular frameworks and libraries. Enrol in Skillfloor's machine learning course today and embark on a rewarding journey to master AI and machine learning technologies.


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₹40,000

₹19,900

Skill Level

Intermediate

Internship

2-month Unpaid

Live Project

10

Certificate

Yes

Live Training

Yes

Career Assistance

Yes

Expiry Period

Lifetime
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Course Highlights

  • Foundations of Machine Learning: Introduces the basic concepts, algorithms, and mathematics underlying machine learning. This serves as the groundwork upon which more complex topics are built.

  • Data Preparation and Cleaning: Covers techniques for preparing and cleaning data to make it suitable for analysis and ML models, recognizing that data quality is crucial for meaningful outcomes.

  • Supervised Learning: Explores a range of algorithms designed to learn from labelled data, including regression and classification, which are foundational to many practical applications of machine learning.

  • Unsupervised Learning: Delves into methods for discovering patterns and structures in data without predefined labels, including clustering and dimensionality reduction, expanding the toolkit for data analysis.

  • Deep Learning: Introduces the fundamentals of deep learning, focusing on neural networks and their applications. This section covers how these models can recognize complex patterns in large datasets.

  • Model Evaluation and Selection: Teaches how to assess the performance of machine learning models using various metrics and techniques, ensuring that the most appropriate model is chosen for a given task.

  • Feature Engineering and Selection: Discusses the importance of selecting and engineering features from raw data to improve model performance, acknowledging that the input to a model significantly affects its output.

  • Ensemble Methods: Explores techniques that combine multiple machine learning models to improve predictions, covering methods like boosting and bagging, which can lead to more robust and accurate models.

  • Ethics and Bias in Machine Learning: Addresses the ethical considerations and potential biases in machine learning applications, stressing the importance of fairness and transparency in ML models.

  • Practical Applications and Case Studies: Offers insights into real-world applications of machine learning across various industries, providing learners with an understanding of how ML techniques are applied to solve actual problems.

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Certification

The Certified Data Science Associate certificate equips individuals with foundational data science skills. Covering key concepts in statistics, programming, and data analysis, this certification validates a practical understanding of data science principles. Ideal for beginners, it fosters competence in handling and interpreting data. A valuable asset for anyone aiming to kickstart a career in data science or enhance their analytical capabilities.

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Top 10 reasons

  1. Machine learning: Earn the respected password of Machine Learning Expert, attesting to your proficiency in advanced machine learning algorithms and methods.

  2. Explore in-depth: subjects like reinforcement learning, deep learning, and supervised and unsupervised learning all of which are crucial for resolving challenging issues in a variety of fields.

  3. Skills in developing: Through real projects and case studies, you may hone your skills in developing and executing machine learning solutions.

  4. Gain knowledge: of the newest developments and top techniques in machine learning research and applications by attending seminars led by professionals in the field.

  5. Industries: Improve your chances of landing a job by gaining specialized expertise that employers in industries like e-commerce, healthcare, and finance greatly value.

  6. Become a member:  peer and mentor-led learning community that supports cooperation and information exchange in the field of machine learning.

  7. knowledgeable teachers: Get individualised advice and criticism from knowledgeable teachers to guarantee a customised learning environment that satisfies your requirements.

  8. Tools: Get access to state-of-the-art frameworks and tools, such as sci-kit-learn, TensorFlow, and PyTorch, to help with your machine-learning projects.

  9. Build your network: Connect with other machine learning experts and enthusiasts to increase your contacts and future employment prospects.

  10. machine learning applications: Get ready to take on real-world problems and spur innovation in machine learning applications to establish yourself as a successful player in this quickly developing industry.

 

Syllabus

  • Overview of Machine Learning Concepts

  • Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

  • Understanding the Machine Learning Workflow

  • Python Basics for Machine Learning

 

  • Data Cleaning Techniques

  • Handling Missing Data

  • Data Transformation and Scaling

  • Feature Selection and Extraction

 

  • Linear Regression

  • Logistic Regression

  • Decision Trees and Random Forests

  • Support Vector Machines

  • k-Nearest Neighbors

 

  •  K-Means Clustering

  •  Hierarchical Clustering

  •  Principal Component Analysis (PCA)

  •  t-Distributed Stochastic Neighbor Embedding (t-SNE)

  •  Association Rule Learning

  • Cross-Validation Techniques

  • Evaluation Metrics: Accuracy, Precision, Recall, F1 Score

  • Confusion Matrix and ROC Curve Analysis

  • Hyperparameter Tuning and Grid Search

  •  Ensemble Learning Techniques

  •  Deep Learning Fundamentals

  •  Convolutional Neural Networks (CNNs)

  •  Recurrent Neural Networks (RNNs)

  •  Transfer Learning and Fine-Tuning

  •  Introduction to NLP and Text Preprocessing

  •  Text Representation Techniques: Bag-of-Words, TF-IDF, Word Embeddings

  •  Sentiment Analysis

  •  Named Entity Recognition (NER)

  •  Text Classification with Deep Learning Models

  •  Basics of Reinforcement Learning

  •  Markov Decision Processes (MDPs)

  •  Q-Learning and Deep Q-Networks (DQN)

  •  Policy Gradient Methods

  •  Applications of Reinforcement Learning in Games and Robotics

 

  •  Model Deployment Strategies

  •  RESTful APIs for Model Serving

  •  Containerization with Docker

  •  Cloud Deployment on AWS, Azure, or Google Cloud Platform

  •  Model Monitoring and Maintenance

 

FAQ

A certified ML expert is someone who has completed a recognized certification program in machine learning, demonstrating their proficiency in understanding and applying ML algorithms and techniques.

Obtaining certification in machine learning can validate your skills, enhance your credibility, and increase your job prospects in fields where ML technology is used.

To become certified, you typically need to complete specific training courses, gain hands-on experience with ML algorithms and tools, and pass an exam that tests your knowledge and proficiency in ML concepts.

Skills such as programming (especially in languages like Python), mathematics (including statistics and linear algebra), data preprocessing, model evaluation, and understanding of ML algorithms are important for ML certification.

With ML certification, you can pursue roles such as data scientist, machine learning engineer, AI researcher, ML consultant, or data analyst in various industries.

 

The time it takes to become certified varies depending on the program and your prior knowledge, but it typically takes several months to a year of study and preparation.

 

Earning machine learning certification can provide you with valuable skills and credentials that can help advance your career and open up new opportunities in the field of ML.

 

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