Certified Machine Learning Specialist (CMLS) Course

The Certified Machine Learning Specialist (CMLS) program provides aspiring data practitioners a high-level overview of data manipulation techniques, concepts, and machine learning algorithms using Python programming language. This CMLS training program at Vinsys trains professionals with the practical skills to implement regression and classification models using supervised learning algorithms such as neural networks, decision trees, linear & logistic regression models, k-nearest neighbors, support vector machines, & ensemble methods such as a random forest.

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  176 Ratings

               365 Participants

Group Discount

Upto 15% OFF

32 hours training

GICT Authorized Training Partner

Intermediate-level Machine Learning Course

Hands-on Labs with ML Tools

Certified Machine Learning Specialist (CMLS) Course Description


This machine-learning specialist course is aimed at aspiring data practitioners, from data analysts to data scientists. In this course, you will learn about topics such as classification and linear regression to more advanced topics such as boosting, ensemble methods, Support Vector Machines (SVM), Hidden Markov Models, and Bayesian Networks.
The Certified Machine Learning Specialist (CMLS) program is an entry-level program that allows you to dive into Machine Learning and Artificial Intelligence – two major players of the next Industrial Revolution. This course will introduce professionals to open-source Machine Learning tools such as WEKA, Rapidminer, Jupyter notebook using packages like scikit-learn. CMLS training also provides an insight on different algorithms in some of the widely adopted Machine Learning methods such as Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
 

Course Curriculum


Audience

  • IT Executives/Managers
  • Risk Analyst/Management
  • Business Analyst
  • Data Analyst
  • Banking Executives/Managers
  • Software Engineers
  • System Engineers

Course Objectives

At the end of this course, participants will:

  • Acquire knowledge of AI and machine learning and its impact on enterprises with several use cases
  • Acquire knowledge on machine learning techniques: Supervised, Unsupervised & Reinforcement Learning
  • Understand the usage of ReLU as a deep learning-activation function and learning rate
  • Gain a solid understanding of discriminative and generative algorithms
  • Gain a solid understanding of key concepts like Principal Component Analysis (PCA), Hyperparameter tuning with Grid Search, Clustering, Classification, Regression & Neural Network

Eligibility Criteria

Participants are recommended to have some basic programming knowledge in any languages.

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Training Options


ONLINE TRAINING

Instructor led Online Training


  • 32 hrs (4 days) inclusive of training and exam
  • Experienced Subject Matter Experts
  • Approved and Quality Ensured training Material
  • 24*7 leaner assistance and support

CORPORATE TRAINING

Customized to your team's need


  • Blended Learning Delivery Model (Self-Paced E-Learning And/Or Instructor-Led Options)
  • Course, Category, And All-Access Pricing
  • Enterprise-Class Learning Management System (LMS)
  • Enhanced Reporting For Individuals And Teams
  • 24x7 Teaching Assistance And Support

Course Outline


  • Definition of machine learning systems
  • Goals of machine learning
  • Machine Learning vs Traditional Statistics
  • Machine learning application and challenge
  • Supervised learning
  • Linear regression
  • Logistic regression
  • Correlation matrix
  • K-Nearest Neighbour
  • Unsupervised learning
  • k-means Clustering Algorithm
  • Association rule
  • Principal Component Analysis
  • Reinforcement learning
  • Discriminative vs Generative Algorithms
  • Naïve bayes
  • Convolutional neural network (CNN)
  • Recurrent Neural Network (RNN)
  • Long Short-Term Memory Network (LSTM)
  • Introduction to deep learning
  • Traditional Machine learning vs deep learning
  • Activation function
  • Loss function
  • Learning rate
  • Hyperparameters and optimization techniques
  • Gridsearch
  • Pessimistic Biased
  • Model selection for non-probabilistic methods
  • Cross validation
  • Introduction to WEKA
  • How to install WEKA
  • The Knowledge Flow interface
  • The Command-Line interface
  • Classification Rules and association Rules
  • Attribute Selection and Fast attribute selection using ranking
  • ID3 based decision tree algorithm
  • Entropy and Information gain
  • ID3 implementation using WEKA
  • Association rule mining using Frequent Pattern (FP) Growth algorithm
  • FP-Tree structure
  • FP-Growth Algorithm
  • Implementation of FP-Growth using WEKA
  • Introduction to tensor flow
  • Tensor flow library
  • Sklearn library
  • Keras library
  • API on Linear regression
  • API on logistic regression
  • API on random forest
  • API on support vector machine (SVM)
  • API on naïve bayes
  • API on k-Nearest Neighbor (kNN)
  • API on k-means Clustering Algorithm
  • API on convolution Neural Network (CNN)
  • API on local outlier
  • API on deep learning auto decoder
  • API on principal component analysis (PCA)
  • Data preprocessing
  • Correlation
  • Association rules
  • k-means Clustering Algorithm
  • Linear regression
  • Logistic regression
  • Neural network
  • Text miming

Course Reviews


FAQ's


Vinsys follows a focused yet flexible training approach that surely increases the learning potential of learners and improves success rates in certification exams. We have matured over 21 years of our training journey and have a proud history of successfully certifying 750,000+ professionals globally. Our GICT approved trainers and courseware are updated as per the latest industry guidelines to provide the best of learning experience to our students.

Yes, you are required to have some fundamental understanding of software development/programming.

The 4-day intensive training program is followed by:

  • Written examination
  • Project work

Participants will need to obtain 70% in both the components – written exam and project work in order to qualify for this certification. If the participant fails one of the components, they will not pass the course and have to re-take that particular failed component. If they fail both components, they will have to re-take the assessment.

Yes. With such a boom in the machine learning and artificial intelligence fields, data practitioners with a globally recognized certification such as the GICT-approved CLMS course elevates the validity of the skills of certified data practitioners. In the coming years, the demand for CMLS professionals is sure to increase.