Machine Learning with Python and TensorFlow, BI techniques using Siebel and BIP, Gain hands-on in diverse projects
Sub Category
- Data Science
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Objectives
- Python and PySpark Fundamentals: Master the basics of Python and PySpark, including programming with RDD, MySQL connectivity, and PySpark joins.
- Intermediate PySpark Techniques: Explore advanced PySpark concepts like linear regression, generalized linear regression, forest regression, etc
- Advanced PySpark Applications: Dive into advanced PySpark applications such as RFM analysis, K-Means clustering, image to text, PDF to text, and Monte Carlo
- Machine Learning with TensorFlow: Gain expertise in TensorFlow for machine learning, covering topics from installation and libraries to data manipulation
- Practical Data Science Projects: Apply your knowledge to real-world projects, including shipping and time estimation, supply chain-demand trends analysis
- Deep Learning and NLP: Understand the fundamentals of deep learning, neural networks, and natural language processing (NLP), with hands-on in keras.
- Bayesian Machine Learning: Learn the principles of Bayesian machine learning, A/B testing, and hierarchical models for multiple variant testing.
- Machine Learning with R: Explore machine learning using R, covering regression, classification, decision trees, support vector machines, dimension reduction
- AWS Machine Learning: Gain insights into Amazon Machine Learning (AML), connecting to data sources, creating ML models, batch predictions, and advanced setting
- Business Intelligence (BI) and Data Warehousing: Understand BI concepts, multidimensional databases, metadata, ETL processes, and various tools in BI
- Deep Dive into Specific BI Topics: Explore specific BI topics such as break-even analysis, multivariate analysis, graphs, cluster analysis, outlier discovery
- Practical Application of Clustering and Regression: Apply clustering algorithms like K-Means and DBSCAN, and delve into regression analysis for market basket
- Comprehensive Data Science Techniques: Cover a wide range of data science techniques, including sequential data analysis, regression models, market basket
- Machine Learning in Business: Understand the strategic imperative of BI, BI algorithms, benefits of BI, information governance, and BI applications in business
- Latest Developments in Machine Learning: Stay updated on new developments in machine learning, the role of data scientists, types of detection in ML
- Business Intelligence Publisher (BIP) using Siebel: Learn to use BIP with Siebel, covering user types, running modes, BIP add-ins, report development
- Business Intelligence (BI): Explore BI frameworks, strategic imperatives, data warehousing, ETL processes, and the role of BI in organizations.
- Advanced BI Concepts: Delve into advanced BI concepts such as semantic technologies, BI algorithms, benefits of BI, and real-world applications
- Meta Data and Project Management: Understand the importance of meta data, essentials for IT, business meta data, project planning, deployment processes
- Statistical and Machine Learning Models: Learn and implement various statistical and machine learning models, including linear regression, decision trees
- Time Series Analysis: Dive into time series analysis, covering topics like moving average models, auto-correlation functions, forecasting using stock prices
- Hands-on Programming and Tools: Gain practical programming experience with tools like TensorFlow, PySpark, R, and BI tools, ensuring hands-on application
- Practical Skills for Data Scientists: Develop practical skills in data science, data analysis, machine learning, deep learning, NLP, and BI
- Real-world Projects and Applications: Work on diverse projects—from predictive modeling and regression analysis to fraud detection and supply chain analysis
- Cloud-based Machine Learning with AWS: Acquire skills in cloud-based machine learning with AWS, covering AML lifecycle, data source connections, ML models
- In-depth Understanding of Neural Networks: Explore the structure of neural networks, activation functions, optimization techniques, and implementation
- Natural Language Processing (NLP) Techniques: Learn text preprocessing, feature extraction, and NLP algorithms, applying them to tasks like sentiment analysis
- Bayesian Machine Learning for A/B Testing: Understand Bayesian machine learning principles for A/B testing, hierarchical models, and practical applications
- Data Warehousing and ETL Processes: Explore data warehousing concepts, ETL design, meta data, and deployment processes, gaining a comprehensive understanding
- Machine Learning in Business and Industry: Gain insights into the strategic imperatives of BI in business, BI algorithms, benefits of BI, and the practicals
Pre Requisites
- No prior knowledge of machine learning required
- Basic knowledge of R tool is an added advantage
- Basic Python and Mathematics (Linear Algebra Basics) is an added advantage
- Computer Access
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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