Description
Master class on machine learning and business analytics. This course is a comprehensive educational course in the field of machine learning and business analytics. This course starts with an introduction to the key parts of machine learning. So we’ll cover the basics of statistics and its applications, the PySpark platform for big data, advanced PySpark topics, and various machine learning techniques using Python and TensorFlow. The course culminates with practical projects in various fields, giving you hands-on experience in applying machine learning in real-world scenarios.
What will you learn at the master class on machine learning and business analytics
- Python and PySpark Basics: Master the basics of Python and PySpark, including programming with RDD, connecting to MySQL, and PySpark extensions.
- Advanced PySpark Techniques: Learn advanced PySpark concepts such as linear regression, generalized linear regression, forest regression and more.
- Advanced PySpark Applications: An in-depth look at advanced PySpark applications such as RFM analysis, K-means clustering, Image to text, PDF to text, and Monte Carlo simulation.
- Machine Learning with TensorFlow: Gain experience using TensorFlow for machine learning, covering topics from setup and libraries to data manipulation.
- Hands-on Data Science Projects: Apply your knowledge to real-world projects such as estimating delivery times, analyzing supply chain demand trends.
- Deep learning and natural language processing (NLP). Learn the fundamentals of deep learning, neural networks, and natural language processing (NLP) with hands-on experience with Keras.
- Bayesian Machine Learning: Learn the fundamentals of Bayesian machine learning, A/B testing, and hierarchical models for multivariate testing.
- Machine Learning Using R: Learn machine learning using R, including regression, classification, decision trees, support vector machines, dimensionality reduction.
- Machine Learning with AWS: Learn about Amazon Machine Learning (AML), connect to data sources, create machine learning models, batch predictions, and advanced customizations.
- Business Intelligence (BI) and Data Warehousing: Understanding of BI concepts, multidimensional databases, metadata, ETL processes and various BI tools.
- Dive into specific BI topics. Learn specific BI topics such as forward analysis, multivariate analysis, graphs, cluster analysis, anomaly detection.
- Practical application of clustering and regression: application of clustering algorithms such as K-Means and DBSCAN, as well as shopping cart regression analysis.
- Comprehensive Data Analysis Techniques: Covers a wide range of data analysis techniques including ordinal data analysis, regression models, shopping carts.
- Machine Learning in Business: Understanding the strategic imperative of BI, BI algorithms, benefits of BI, information management and applications of BI in business.
- Latest Developments in Machine Learning: Stay updated with new developments in machine learning, role of data scientists, types of diagnostics 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): An overview of BI frameworks, strategic imperatives, data warehousing, ETL processes, and the role of BI in organizations.
- Advanced BI Concepts: An in-depth look at advanced BI concepts such as semantic technologies, BI algorithms, BI benefits, and practical applications.
- Metadata and Project Management: Understanding the importance of metadata, IT requirements, business metadata, project planning, deployment processes.
- Statistical Models and Machine Learning: Study and implement various statistical models and machine learning, including linear regression and decision trees.
- Time Series Analysis: Covers topics such as moving average models, autocorrelation functions, forecasting using stock prices.
- Practical programming and tools. Gain hands-on programming experience using tools like TensorFlow, PySpark, R and BI and provide hands-on application.
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Practical Skills for Data Scientists: Develop practical skills in data science, data analytics, machine learning, deep learning, NLP and business analytics.
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Real projects and applications. Work on projects ranging from predictive modeling and regression analysis to fraud detection and supply chain analysis.
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Cloud Machine Learning with AWS: Master cloud machine learning with AWS, including the AML lifecycle, data source connections, and machine learning models.
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Deep understanding of neural networks: Learn the structure of neural networks, activation functions, optimization and implementation techniques.
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Natural language processing (NLP) methods. Learn text preprocessing, feature extraction, and NLP algorithms and apply them to tasks such as sentiment analysis.
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Bayesian Machine Learning for A/B Testing: Learn the fundamentals of Bayesian machine learning for A/B testing, hierarchical models, and practical applications.
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Data Warehousing and ETL Processes: Learn and gain a thorough understanding of data warehousing concepts, ETL design, metadata, and deployment processes.
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Machine Learning in Business and Industry: Gain insight into the strategic imperatives of BI in business, BI algorithms, benefits of BI, and practical examples.
This course is suitable for people who
- Aspiring Data Scientists: People who want to build a career in data science, machine learning, and data analytics.
- Data Analysts: Professionals who want to improve their data management and analysis skills to gain actionable insights.
- Software Engineers: Those interested in transferring or updating skills to work on data-driven projects using Python, PySpark, TensorFlow and R.
- Business Intelligence (BI) Professionals: People seeking to integrate machine learning and advanced analytics into business intelligence practices.
- Undergraduate and Graduate: Individuals majoring in computer science, data science, or related fields with an interest in machine learning.
- IT and database management professionals: seek to expand their expertise by understanding the practical applications of machine learning.
- Anyone interested in data-driven decision making: People from diverse backgrounds who are passionate about using data for informed decision-making processes.
- The course covers a wide range of areas and provides an introduction to advanced knowledge, making it suitable for both beginners and those with little experience in data-related fields.
Details of the course “Master class on machine learning and business analytics”
- Publisher: Udemy
- Lecturer: EDUCBA Bridging the Gap
- Level of training: from beginner to advanced
- Duration of training: 72 hours 5 minutes
- Number of courses: 522
Course topics for 3/2024
Prerequisites for the Machine Learning and Business Analytics Masterclass
- No prior knowledge of machine learning is required.
- Basic knowledge of the R tool is an added advantage.
- Basic level of Python and mathematics (basic linear algebra) is an added advantage.
- computer access
course images
Example video course
installation instructions
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English subtitles
Quality: 720p
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Password for file(s): www.downloadly.ir
size
24.3 GB