Machine Learning and Business Intelligence Masterclass
Machine Learning with Python and TensorFlow, BI techniques using Siebel and BIP, Gain hands-on in diverse projects
4.48 (1392 reviews)
49,682
students
72.5 hours
content
Mar 2024
last update
$19.99
regular price
What you will learn
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
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1978034
udemy ID
10/20/2018
course created date
5/29/2019
course indexed date
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