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Machine Learning Tutorial
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Naive Bayes in Machine Learning
    • Dataset Labelling
    • DataSet Example
    • Deep Learning Techniques
    • Dataset ZFS
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • What is Kernel in Machine Learning
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Machine Learning Certifications
    • Machine Learning Python vs R
    • Optimization for Machine Learning
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Support Vector Machine in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • Dataset for Linear Regression
    • Decision tree limitations
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Apriori Algorithm in Machine Learning
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • Hierarchical Clustering Agglomerative
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Automation Anywhere Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • Deep Learning
    • What Is Deep learning
    • Overviews Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

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Machine Learning Certifications

By Nilam LambadeNilam Lambade

Machine Learning Certifications

Introduction to Machine Learning Certifications

Did reading about Machine Learning interest you? Are you considering a career in Artificial Intelligence and Machine Learning?  Here are some basic things you need to know before we get started on the certifications that will help you carve a career in Artificial Intelligence and Machine Learning.

Machine Learning Certifications

One can wonder why learning is an important factor in Artificial Intelligence and Machine Learning. Machine Learning Certifications train computers to think like humans while handling data. However, the input that goes into this has to be extremely advanced and good for it to match human talents. This requires the coding and algorithms to be precise and capable of extracting patterns that are not visible to the human eye. Hence, the educational background and the skillset of Machine Learning Engineers are very important. People who opt for a career in Machine Learning preferably have a bachelor’s or masters in Computer Science along with statistics. To add to this, there is a whole list of certifications that one can do to become a Machine Learning Engineer.

Below is a list of all the tools and skills that one can gain to be a Machine Learning Engineer.

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Machine Learning Certifications

Machine Learning Certifications (Courses)

1. Machine Learning by Andrew Ng

Andrew Ng is a computer scientist who started the course Machine Learning Certifications by Andrew Ng which is available on Coursera. The course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.  The course is an eleven-week course.

Features/Prospects:

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4.7 (86,471 ratings)

This course covers the concepts of regression, machine learning technology and methods. The course uses Octave which is a good tool to test the algorithm that has been built before it is used. The highlight of this course is that it gives candidates a detailed idea of the mathematical and statistical logic that goes behind the algorithms built-in machine learning. Structured in a way that the course starts with the basics of mathematics and statistics and works its way to more advanced concepts of Machine Learning Certifications technology and methods like the discussion of methods like neural networks and anomaly detection, machine learning system design and algorithms like support vector machine to name a few. The course comes with an application example. On the whole, it is designed to give students a strong foundation in machine learning.

Website: https://www.coursera.org/learn/machine-learning

Languages: English with subtitles in Spanish, Hindi, Japanese, Chinese

Course Creator: Andrew Ng, Stanford University

Average User Rating: 4.9/5

2. Machine Learning Specialization by Coursera

Machine Learning: Regression course in Coursera put together by the University of Washington explores regularized linear regression models for the task of prediction and feature selection.  This course enables candidates with the right skills to handle large data sets and handle identify the correct model from a range of models of different complexity. Algorithms are used to scale these models to large datasets. This is a six-week course.

Features/Prospects:

This course focuses on the various regression methods that are applied in machine learning. Regression plays a crucial role in Machine Learning algorithm building. Hence the course is designed to give beginners as well as experienced a thorough understanding of the Statistical regression methods starting with simple linear regression, multiple regression, and tougher concepts like Assessing performance, Ridge Regression, Feature selection, and Lasso and Nearest neighbors and Kernel regression. If completed successfully, this course can equip candidates with the right skills to build statistically strong algorithms.

Website: https://www.coursera.org/learn/ml-regression

Languages: English

Course Creator: University of Washington

Average User Rating: 4.8/5

3. Machine Learning by Data Camp

Introduction to Machine Learning by Data Camp is the right course for those who have a good understanding of R and statistics but are beginners with Machine Learning.  The course is a collection of very important Machine Learning Certifications Tasks like classification, regression, and clustering.

Features/Prospects:

The course on Machine Learning by Data Camp is a very well-structured and precise one. The course is divided into simple sub-divisions which each give candidates a thorough understanding of what Machine Learning is, the performance measures, classification, regression concepts, and the statistical concept of clustering. A reasonable understanding of R and statistics is a prerequisite. Once completed, this course gives candidates adequate skills to practice Machine Learning algorithm building.

Website: https://www.datacamp.com/courses/understanding-machine-learning

Languages: English

Course Creator: Data Camp

Average User Rating: Not Available

4. Machine Learning Specialization by EDUCBA

Become a Machine Learning Specialist is a course by EDUCBA, a global skill-based education provider and an initiative of IIT IIM India alumni. The course covers the essentials to learn and master Machine Learning to enable Engineers to program computers to analyse data automatically. The course enables candidates to acquire skills to analyse data using R, Python, and MatLab. Also helps in extracting meaningful insights from huge chunks of data and making accurate predictions for decision making.

Features/Prospects:

This course on becoming a Machine Learning Certifications specialist focuses on the essentials of statistics, statistical tools, Machine learning concepts, technology, and methods. In a nutshell, it covers all the topics necessary for a person to become a machine learning specialist. Upon completion, it is sure to help by equipping candidates with the right skills to carry out machine learning algorithm building based on sound logic. It is good for people who want to learn Machine Learning Certifications quickly in the casework demands for it.

Website: https://www.educba.com/

Languages: English

Course Creator: EDUCBA

Average User Rating: Not Available

5. Machine Learning by Simplilearn

Machine Learning Certifications Course by Simplilearn, a certification provider will make you an expert in machine learning and enable candidates with the skill to master Machine Learning Certifications concepts and techniques which includes supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling. Introduction to Artificial Intelligence and Machine Learning.

Features/Prospects:

This course is an in-depth and thorough collection of all topics necessary for a person to become a machine learning engineer. One might require patience and a reasonable inclination towards learning to complete this course as it covers topics extensively all topics. It starts with the basics of what Machine Learning Certifications are and their applications, the techniques of machine learning, Data, Math, regression, classification, clustering, and deep-learning. It is a good course for beginners as well as experienced to get a genuine understanding of the subject.

Website: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course

Languages: English

Course Creator: Simplilearn

Average User Rating: Not Available

6. Intro to Machine Learning by Udacity

This course was put together by udacity, a global certification provider. The course is designed to teach candidates the end-to-end process of investigating data through a machine learning lens. It teaches to extract meaningful information from your data and to evaluate the performance of Machine Learning Algorithms. The course is structured in the following manner.

Features/Prospects:

Courses on technology and data science on Udacity are usually very well received. This applies to its course on machine learning. The course covers basic concepts, Machine Learning Certifications methods like Naïve Bayes, SVM, Decision Trees, choosing an algorithm, Data, Regression, Outliers, Clustering, Feature scaling, Text learning, feature selection, PCA, Validation, and Evaluation metrics. It is a good course to get a step-by-step understanding of the whole analysis and all the hurdles that you need to keep in mind while programming the computer to carry out the analysis task.

Website: https://classroom.udacity.com/courses/ud120

Languages: English

Course Creator: Udacity

Average User Rating: Not Available

7. Machine Learning by Upgrad

PG Diploma in Machine Learning Certifications and AI by Upgrad is a course designed to master advanced machine learning Certifications and artificial intelligence concepts such as Neural Networks, Natural Language Processing, Graphical Models, and Reinforcement Learning. It also helps in learning cutting-edge applications through projects created in collaboration with the industry: Chat Bots, Image Classifiers, and more. It is an online eleven-month course and is structured in the following manner.

Features/Prospects:

The course on Machine Learning and Artificial Intelligence in Upgrad focuses on the technology, methods, and logic that goes into algorithm building rather than starting from the basic concepts. It covers Math, Statistical skills, Regression, and ML methods. It is a good course for those who have a basic understanding of what Machine Learning is and want to explore further and widen their knowledge on the subject.

Website: https://upgrad.com/machine-learning-and-artificial-intelligence/?iref=NavBar_Program_MlAndAi

Languages: English

Course Creator: Upgrad

Average User Rating: Not Available

Conclusion

Having an idea of the different certifications available, candidates can select the one most suited to their career requirements and their preferences. Even though structured differently, all courses speak of the same skillsets and are capable of equipping learners with various concepts and techniques to handle the current data trends.

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