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Certified Machine Learning Master (CMLM)

About Certification

GSDC's Certified Machine Learning Master certification offers a deep understanding of Machine Learning practices. Machine learning is a subset of Artificial Intelligence that is based on the study of computer algorithms that has the ability to improve automatically through experience. Machine learning algorithms make a model with sample data or training data for prediction and decision making without being programmed to do so.

Certified Machine Learning Master certification doesn't only clear your concept about the basics of Machine Learning but gives you a detailed picture of all the modules of supervised learning, Apriori Algorithm, Market Basket Analysis, ARIMA analysis and many more. After the completion of Machine Learning Master certification, you will be able to worth all the machine learning models and use every Machine Learning approaches.

Certification badge for Machine Learning Master
 

Objectives

Certified Machine Learning Master certification shares a deep understanding of:

  1. The basics of Machine Learning
  2. The best practices of machine learning
  3. Various machine learning modules
  4. Machine learning approaches
  5. Different Machine learning algorithms
  6. Future scopes and to trends of machine learning

 

Target Audience

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IT Professionals

Software Developers

Process Managers

Application Developers

Project Managers

Data Analysis Professionals

Web Developers

 

Benefits

Few benefits of getting a Machine Learning Master certification are:

Validation of your skills

Higher salary structure

Broadened up career choices

Practical skills to implement Machine Learning in real life

Improved potential to become a part of the era of Artificial Intelligence

 

Pre-requisites

There are no pre-requisites for getting certified as a Machine Learning Master. Although, basic knowledge and working knowledge of programming and statistics will be required.

 

Examination

Multiple-choice exam of 40 marks.
You need to acquire 26+ marks to clear the exam.
In case the Participant failed then they will be free 2nd attempt.
Re-examination can be taken up to 30 days from the date of the 1st exam attempt.

 

Sample Certificate

 

Exam Syllabus

1. Introduction to Python Programming

  • Overview of Python
  • History of Python
  • Python Basics : variables, identifiers, indentation
  • Data Structures in Python (list, string, sets, tuples, dictionary)
  • Statements in Python (conditional, iterative, jump)
  • OOPS concepts
  • Exception Handling
  • Regular Expression
2.Introduction to various packages and related functions
  • Numpy, Pandas and Matplotlib
  • Pandas Module
  • Series
  • Data Frames
  • Numpy Module
  • Numpy arrays
  • Numpy operations
  • Matplotlib module
  • Plotting information
  • Bar Charts and Histogram
  • Box and Whisker Plots
  • Heatmap
  • Scatter Plots
3. Data Wrangling using Python
  • NumPy : Arrays
  • Data Operations (Selection , Append , Concat , Joins)
  • Univariate Analysis
  • Multivariate Analysis
  • Handling Missing Values
  • Handling Outliers
4. Introduction to Machine Learning with Python
  • What is Machine Learning?
  • Introduction to Machine Learning
  • Types of Machine Learning
  • Basic Probability required for Machine Learning
  • Linear Algebra required for Machine Learning
5.Supervised Learning : Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Assumptions of Linear Regression
  • Polynomial Regression
  • R2 and RMSE

6.Supervised Learning : Classification

  • Logistic Regression
  • Decision Trees
  • Random Forests
  • SVM
  • Nave Bayes
  • Confusion Matrix
7.Dimensionality Reduction
  • PCA
  • Factor Analysis
  • LDA
8. Unsupervised Learning : Clustering
  • Types of Clustering
  • K-means Clustering
  • Agglomerative Clustering
9.Additional Performance Evaluation and Model Selection
  • AUC / ROC
  • Silhouette coefficient
  • Cross Validation
  • Bagging
  • Boosting
  • Bias v/s Variance
10. Recommendation Engines
  • Need of recommendation engines
  • Types of Recommendation Engines
  • Content Based
  • Collaborative Filtering
11. Association Rules Mining
  • What are Association Rules?
  • Association Rule Parameters
  • Apriori Algorithm
  • Market Basket Analysis
12. Time Series Analysis
  • What is Time Series Analysis?
  • Importance of TSA
  • Understanding Time Series Data
  • ARIMA analysis
13. Reinforcement Learning
  • Understanding Reinforcement Learning
  • Algorithms associated with RL
  • Q-Learning Model
  • Introduction to Artificial Intelligence
14. Artificial Neural Networks and Introduction to Deep Learning
  • History of Neural Network
  • Perceptron
  • Forward Propagation
  • Introduction to Deep Learning


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