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Niltech Edu is part of a product development company, so you get to experience the technologies we work on. Our Python Developer has build several products fro organisations like DRDO, Indian Airforce and many more using Python Programming. At Niltech Edu our quality curriculum is designed with top-tier industries in mind, not academics, so you learn the high-impact skills that top companies want.
Real-Time Projects Development in Course:
- 1. Introduction to data science
- 2. Data Science vs Data Analysis vs ML vs AI
- 3. Cloud Computing (AWS, GCP)
- 4. Necessary python modules
- 5. Statistical data types
- 6. Mathematical Introduction
- 7. Exploration Data Analysis (EDA)
- 8. Data preprocessing
- 9. Project 1
- 10. Machine Learning and Types
- 11. Linear Regression
- 12. Multi Linear Regression
- 13. Project 2
- 14. Perceptron
- 15. Decision Trees
- 16. Project 3
- 17. Naive Bayes
- 18. Project 4
- 19. Support Vector Machines
- 20. Ensemble Methods
- 21. Major Project 1
- 22. Clustering
- 23. Project 5 - Clustering of movie ratings
- 24. Hierarchical Clustering
- 25. Gaussian Mixture Model (GMM)
- 26. Dimensionality Reduction
- 27. Major Project 2
- 28. Text Analytics
- 29. Video recommender system
- 30. Project 6
- 31. Computer Vision
- 32. Neural Networks
- 33. Deep Neural Networks
- 34. Convolutional Neural Networks
- 35. Project 7
- 36. Project Major 3
- 37. Time Series
- 38. LSTM
- 39. HTML/CSS
- 40. FastAPI
- 41. Cloud deployment
- 42. Cloud ML services
- FInal Project
About Data Science and Machine Learning
At its core, Data Science is a field of study that aims to use a scientific approach to extract meaning and insights from data. Generally, Data Science described as “a combination of information technology, modeling, and business management”. Industrieshave acknowledged the importance of the data science field and have created online data science graduate programs.
Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data. These techniques produce results that perform well without programming explicit rules.
Data science and machine learning are both very popular buzzwords today. These two terms are often thrown around together but should not be mistaken for synonyms. Although data science includes machine learning, it is a vast field with many different tools.
Machine learning creates a useful model or program by autonomously testing many solutions against the available data and finding the best fit for the problem. This means machine learning can be great for solving problems that are extremely labor intensive for humans. It can inform decisions and make predictions about complex topics in an efficient and reliable way.
Though it may sound obvious, data science relies on data. The massive growth of data science was spurred by the availability of massive datasets and cheap computing power. Only with these incredible resources is data science effective. Small datasets, messy data, and incorrect data can waste a lot of time, creating models that produce meaningless or misleading results. If the data doesn’t capture the actual cause of variation, data science will fail.
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