![Data Science, Machine Learning & AI](https://images.credly.com/images/724a94dd-752a-4555-bc4d-1e71089f4843/Data_Science_Badge_01-01.png)
- Type Certification
- Level Intermediate
- Time Months
- Cost Paid
Data Science, Machine Learning & AI
Issued by
TECH I.S. Programming School
This badge represents a TECH I.S. graduate’s skill in Machine Learning. On earning this badge the student has a good understanding of various supervised and unsupervised learning algorithms. Students work on projects where they use supervised and unsupervised learning algorithms to identify and extract patterns from data. These projects are built using real-world data. Students work on a variety of problems like Regression, Classification, Clustering, NLP, and Image Recognition.
- Type Certification
- Level Intermediate
- Time Months
- Cost Paid
Skills
- Analytics
- Apache Spark
- Data Processing and Visualisation
- Data Science
- Decision Trees/Ensemble Methods
- Deep Neural Networks
- Excel
- Exploratory Data Analysis
- Git
- GitHub
- Linear Algebra
- Linear Regression Models
- Machine Learning
- Model Deployment
- Model Optimisation
- MySQL
- Natural Language Processing
- Neural Networks
- Python
- Recommendation System
- SQL
- Statistics
Earning Criteria
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Completed the Data Science, Machine Learning & AI course by passing assessments after each module in the program.
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Cleared assessments for Python libraries like Pandas, Numpy, Matplotlib, seaborn, and SkLearn, Tensorflow, and Keras.
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Completed projects on Supervised Learning algorithms like Linear and Logistic Regression, KNN, and Naive Bayes, Decision trees, and Random forest Algorithms, Bagged ensemble models, and boosting algorithms -
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Passed assessments and projects on the deployment of models using the python flask framework.
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Completed projects and has cleared assessments on the use of clustering and Principal Component Analysis.
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Proficiency in dealing with Natural Language Processing problems like text classification and Natural Language Understanding.
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Completed projects and has cleared assessments on Neural Networks for pattern recognition and worked on image classification problems using CNN.