Udemy – Machine Learning and Business Intelligence Masterclass 2019-10

Udemy – Machine Learning and Business Intelligence Masterclass 2019-10 Downloadly IRSpace

Apr 17, 2025 - 20:04 Updated: Apr 21, 2025 - 10:07

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Udemy – Machine Learning and Business Intelligence Masterclass 2019-10

Machine Learning and Business Intelligence Masterclass course. This course is a comprehensive educational path in the field of machine learning and business intelligence. This course starts by taking you through the key parts of machine learning. In this way, we will cover the basics of statistics and its applications, the PySpark framework for processing large data, advanced PySpark topics, and various machine learning techniques using Python and TensorFlow. The course concludes with hands-on projects in various domains, giving you hands-on experience in applying machine learning to real-world scenarios.

What you will learn in the Machine Learning and Business Intelligence Masterclass

Python and PySpark Basics: Mastering the basics of Python and PySpark, including programming with RDDs, connecting to MySQL, and PySpark extensions
Advanced PySpark Techniques: Explore advanced PySpark concepts such as linear regression, generalized linear regression, forest regression, and more.
Advanced PySpark Applications: In-depth review of advanced PySpark applications such as RFM analysis, K-Means clustering, image-to-text conversion, PDF-to-text conversion, and Monte Carlo simulation.
Machine Learning with TensorFlow: Gain expertise in TensorFlow for machine learning, covering topics from setup and libraries to data manipulation.
Practical data science projects: apply your knowledge to real projects such as shipping time estimation, demand trend analysis in the supply chain.
Deep Learning and Natural Language Processing (NLP): Understand the fundamentals of deep learning, neural networks and natural language processing (NLP) with hands-on experience in Keras.
Bayesian Machine Learning: Learn the basics of Bayesian machine learning, A/B testing, and hierarchical models for multivariate testing.
Machine Learning with R: Exploring machine learning using R, including regression, classification, decision trees, support vector machines, dimension reduction.
Machine Learning with AWS: Learn about Amazon Machine Learning (AML), connect to data sources, create machine learning models, batch predictions, and advanced settings.
Business Intelligence (BI) and Data Warehousing: Understanding BI concepts, multidimensional databases, metadata, ETL processes and various tools in BI
Dive into specific BI topics: Explore specific BI topics such as head-to-head analysis, multivariate analysis, graphs, cluster analysis, anomaly detection.
Practical application of clustering and regression: Applying clustering algorithms such as K-Means and DBSCAN, as well as regression analysis for the shopping cart.
Comprehensive Data Science Techniques: Covers a wide range of data science techniques, including ordinal data analysis, regression models, shopping carts.
Machine learning in business: Understanding the strategic imperative of BI, BI algorithms, BI benefits, information governance and BI applications in business.
Latest Developments in Machine Learning: Stay updated on new developments in machine learning, role of data scientists, types of diagnosis in machine learning.
Business Intelligence Publisher (BIP) using Siebel: Learn to use BIP with Siebel, including user types, execution modes, BIP plugins, report development.
Business Intelligence (BI): Review of BI frameworks, strategic imperatives, data warehousing, ETL processes, and the role of BI in organizations.
Advanced BI Concepts: In-depth review of advanced BI concepts such as semantic technologies, BI algorithms, BI benefits, and real-world applications.
Metadata and Project Management: Understanding the importance of metadata, IT requirements, business metadata, project planning, deployment processes.
Statistical models and machine learning: Learning and implementing various statistical models and machine learning, including linear regression, decision trees.
Time Series Analysis: Covers topics such as moving average models, autocorrelation functions, forecasting using stock prices.
Hands-on programming and tools: Gain hands-on programming experience with tools like TensorFlow, PySpark, R, and BI tools and ensure practical application.
Practical Skills for Data Scientists: Develop practical skills in data science, data analytics, machine learning, deep learning, NLP and BI.
Real Projects and Applications: Work on a variety of projects – from predictive modeling and regression analysis to fraud detection and supply chain analysis.
Cloud-Based Machine Learning with AWS: Master cloud-based machine learning with AWS, covering the AML lifecycle, data source connections, ML models
Deep Understanding of Neural Networks: Explore the structure of neural networks, activation functions, optimization and implementation techniques.
Natural Language Processing (NLP) Techniques: Learn text preprocessing, feature extraction, and NLP algorithms and apply them to tasks like sentiment analysis.
Bayesian Machine Learning for A/B Testing: Learn the basics of Bayesian Machine Learning for A/B testing, hierarchical models and practical applications
Data Warehousing and ETL Processes: Explore and gain a comprehensive understanding of data warehousing concepts, ETL design, metadata, and deployment processes.
Machine Learning in Business and Industry: Gain insights into the strategic imperatives of BI in business, BI algorithms, BI benefits, and practical cases.

This course is suitable for people who

Aspiring data scientists: People looking to build a career in data science, machine learning, and data analytics.
Data Analysts: Professionals looking to improve their skills in managing and analyzing data to gain actionable insights.
Software Engineers: Those interested in transferring or upgrading skills to work on data-driven projects using Python, PySpark, TensorFlow, and R.
Business Intelligence (BI) Professionals: Individuals seeking to integrate machine learning and advanced analytics into business intelligence practices.
Students and Graduates: Those majoring in computer science, data science, or related fields with an interest in machine learning.
IT and database management professionals: looking to expand their expertise by understanding the practical applications of machine learning.
Anyone interested in data-driven decision making: People from a variety of backgrounds who are passionate about using data for informed decision-making processes.
The course covers a wide range of areas and provides introductory to advanced knowledge, making it suitable for beginners as well as those with little experience in data-related fields.