Machine Learning and Business Intelligence Masterclass from Udemy

Course Introduction:

Welcome to the Machine Learning Mastery course, a comprehensive journey through the key aspects of machine learning. In this course, we'll delve into the essentials of statistics, explore PySpark for big data processing, advance to intermediate and advanced PySpark topics, and cover various machine learning techniques using Python and TensorFlow. The course will culminate in hands-on projects across different domains, giving you practical experience in applying machine learning to real-world scenarios.

Section 1: Machine Learning - Statistics Essentials

This foundational section introduces you to the world of machine learning, starting with the basics of statistics. You'll understand the core concepts of machine learning, its applications, and the role of analytics. The section progresses into big data machine learning and explores emerging trends in the field. The statistics essentials cover a wide range of topics such as data types, probability distributions, hypothesis testing, and various statistical tests. By the end of this section, you'll have a solid understanding of statistical concepts crucial for machine learning.

Section 2: Machine Learning with TensorFlow for Beginners

This section is designed for beginners in TensorFlow and machine learning with Python. It begins with an introduction to machine learning using TensorFlow, guiding you through setting up your workstation, understanding program languages, and using Jupyter notebooks. The section covers essential libraries like NumPy and Pandas, focusing on data manipulation and visualization. Practical examples and hands-on exercises will enhance your proficiency in working with TensorFlow and preparing you for more advanced topics.

Section 3: Machine Learning Advanced

Advancing from the basics, this section explores advanced topics in machine learning. It covers PySpark in-depth, delving into RFM analysis, K-Means clustering, and image to text conversion. The section introduces Monte Carlo simulation and applies machine learning models to solve complex problems. The hands-on approach ensures that you gain practical experience and develop a deeper understanding of advanced machine learning concepts.

Section 4-7: Machine Learning Projects

These sections are dedicated to hands-on projects, providing you with the opportunity to apply your machine learning skills in real-world scenarios. The projects cover shipping and time estimation, supply chain-demand trends analysis, predicting prices using regression, and fraud detection in credit payments. Each project is designed to reinforce your understanding of machine learning concepts and build a portfolio of practical applications.

Section 8: AWS Machine Learning

In this section, you'll step into the world of cloud-based machine learning with Amazon Machine Learning (AML). You'll learn how to connect to data sources, create data schemes, and build machine learning models using AWS services. The section provides hands-on examples, ensuring you gain proficiency in leveraging cloud platforms for machine learning applications.

Section 9: Deep Learning Tutorials

Delving into deep learning, this section covers the structure of neural networks, activation functions, and the practical implementation of deep learning models using TensorFlow and Keras. It includes insights into image classification using neural networks, preparing you for more advanced applications in the field.

Section 10: Natural Language Processing (NLP) Tutorials

Focused on natural language processing (NLP), this section equips you with the skills to work with textual data. You'll learn text preprocessing techniques, feature extraction, and essential NLP algorithms. Practical examples and demonstrations ensure you can apply NLP concepts to analyze and process text data effectively.

Section 11: Bayesian Machine Learning - A/B Testing

This section introduces Bayesian machine learning and its application in A/B testing. You'll understand the principles of Bayesian modeling and hierarchical models, gaining insights into how these methods can be used to make informed decisions based on experimental data.

Section 12: Machine Learning with R

Designed for those interested in using R for machine learning, this section covers a wide range of topics. From data manipulation to regression, classification, clustering, and various algorithms, you'll gain practical experience using R for machine learning applications. Hands-on examples and real-world scenarios enhance your proficiency in leveraging R for data analysis and machine learning.

Section 13: BIP - Business Intelligence Publisher using Siebel

This section focuses on Business Intelligence Publisher (BIP) in the context of Siebel applications. You'll learn about different user types, running modes, and BIP add-ins. Practical examples and demonstrations guide you through developing reports within the Siebel environment, providing valuable insights into the integration of BI tools in enterprise solutions.

Section 14: BI - Business Intelligence

The final section explores the broader landscape of Business Intelligence (BI). Covering multidimensional databases, metadata, ETL processes, and strategic imperatives of BI, you'll gain a comprehensive understanding of the BI ecosystem. The section also touches upon BI algorithms, benefits, and real-world applications, preparing you for a holistic view of business intelligence.

Each section in the course builds upon the previous one, ensuring a structured and comprehensive learning journey from fundamentals to advanced applications in machine learning and business intelligence. The hands-on projects and practical examples provide you with valuable experience to excel in the field.

What's inside

Learning 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
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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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Show less

Syllabus

Machine Learning - Statistics Essentials

Overview of Machine Learning Certification

Machine Learning Introduction

Introduction to Machine Learning with Python

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers Python, PySpark, TensorFlow, and R, which are essential tools for data scientists working with machine learning and business intelligence

Includes coverage of AWS Machine Learning, which is valuable for those looking to deploy machine learning models in a cloud environment

Explores Business Intelligence Publisher (BIP) using Siebel, which is relevant for professionals working with Siebel applications and reporting

Features hands-on projects in shipping estimation, supply chain analysis, and fraud detection, providing practical experience in applying machine learning

Includes a section on Bayesian machine learning and A/B testing, which is useful for making data-driven decisions based on experimental data

Requires familiarity with Siebel applications for the Business Intelligence Publisher (BIP) section, which may limit its accessibility to some learners

Reviews summary

Comprehensive machine learning and bi overview

According to students, this course offers a remarkably broad overview covering a wide array of topics from foundational statistics and various machine learning techniques using TensorFlow, PySpark, and R, to deep learning, NLP, and even sections on Business Intelligence and AWS ML. Many appreciate the inclusion of practical projects which help solidify learning. However, some learners note that while the breadth is a strength, it can sometimes come at the cost of depth in advanced areas, leading to an experience that might be overwhelming or feel rushed in certain sections. Setup and prerequisites are also mentioned as potential hurdles.

Covers a vast range of topics but depth varies.

"The sheer amount of material covered is impressive, from stats to ML libraries to BI."

"I liked the broad overview of many different ML areas and tools."

"While it covers a lot, some sections could use more in-depth explanations, especially in advanced topics."

"It's a great survey course, but don't expect mastery in all areas; some parts feel superficial."

Hands-on projects are valuable for application.

"The hands-on coding and projects are the strongest part of the course for me."

"Applying the concepts in the real-world projects like fraud detection was very helpful."

"I really benefited from the practical exercises after the theoretical parts."

Business Intelligence sections lack depth.

"The Business Intelligence parts felt less developed compared to the Machine Learning content."

"I was hoping for more depth on BI frameworks and tools beyond just the Siebel part."

"Feels primarily like an ML course with only a basic introduction to BI."

Quality and clarity vary between sections.

"Some lectures were very clear and engaging, while others felt a bit rushed or harder to follow."

"I noticed a difference in presentation style and detail level across different modules."

"Hope they update some parts; the AWS section felt a little outdated."

May be challenging for absolute beginners.

"I struggled a bit initially as it assumes some prior knowledge of programming and statistics."

"Definitely not for complete beginners. Have some Python and math under your belt."

"The pace is fast in some sections, making it hard if you lack prerequisites."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning and Business Intelligence Masterclass with these activities:

Review Statistics Fundamentals

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Reinforce your understanding of fundamental statistical concepts, which are crucial for understanding machine learning algorithms and interpreting results.

Browse courses on Statistics

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Review key statistical concepts like mean, median, mode, standard deviation, and variance.
Practice solving problems related to probability distributions and hypothesis testing.
Familiarize yourself with different types of data and their appropriate statistical treatments.

Review 'Business Intelligence For Dummies'

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Gain a foundational understanding of business intelligence principles and their application in real-world scenarios.

View Casino Gambling For Dummies on Amazon

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Read the chapters covering the core concepts of business intelligence.
Identify the key components of a BI system and their roles.
Understand how BI can be used to support strategic decision-making.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Gain a deeper understanding of machine learning algorithms and their implementation using popular Python libraries.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters relevant to the topics covered in the course.
Work through the code examples and exercises provided in the book.
Experiment with different parameters and datasets to gain a better understanding of the algorithms.

Four other activities

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Complete Machine Learning Challenges on Kaggle

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Improve your machine learning skills by participating in Kaggle competitions and working on real-world datasets.

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Choose a Kaggle competition that aligns with your interests and skill level.
Download the dataset and explore it to understand its structure and content.
Develop a machine learning model to solve the problem posed by the competition.
Submit your predictions and compare your performance to other participants.
Learn from the solutions and approaches used by top-performing participants.

Create a Blog Post on a Machine Learning Topic

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Solidify your understanding of a specific machine learning topic by explaining it in a clear and concise manner.

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Choose a machine learning topic that you find interesting or challenging.
Research the topic thoroughly and gather information from various sources.
Write a blog post that explains the topic in a way that is easy to understand.
Include examples and visualizations to illustrate the concepts.
Publish your blog post on a platform like Medium or your own website.

Develop a Predictive Model for Customer Churn

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Apply your machine learning skills to a real-world problem by building a model to predict customer churn.

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Gather and preprocess customer data from various sources.
Explore different machine learning algorithms to identify the best model for predicting churn.
Evaluate the performance of your model and fine-tune it to improve accuracy.
Deploy your model and monitor its performance over time.

Build a Data Visualization Dashboard

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Develop your data visualization skills by creating an interactive dashboard to present key business insights.

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Choose a dataset relevant to business intelligence, such as sales data or customer data.
Select a data visualization tool like Tableau or Power BI.
Design and create interactive charts and graphs to present key metrics and trends.
Build a dashboard that allows users to explore the data and gain insights.
Present your dashboard to stakeholders and gather feedback.

Career center

Learners who complete Machine Learning and Business Intelligence Masterclass will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models and algorithms. This course provides a strong foundation for a machine learning engineer. The course covers essential statistical concepts, as well as hands-on experience with TensorFlow, PySpark, and R, which are some of the most common tools of the trade. Practical projects in this course, covering topics such as prediction, regression, and fraud detection, will equip you with real-world skills highly applicable to the work of a machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A data scientist analyzes complex data, develops statistical models, and communicates findings to stakeholders. This course provides a strong background for a data scientist by covering topics such as Python, PySpark, R, machine learning algorithms, and business intelligence concepts. This course will provide practical experience through hands-on projects dealing with various types of data and applications. A data scientist will find the broad coverage of machine learning techniques, statistical testing and modeling useful.

See salaries and explore the career path for Data Scientist

Business Intelligence Analyst

A business intelligence analyst examines business data and trends to provide actionable insights that inform strategic decisions. This course may be particularly useful for a business intelligence analyst by exploring multidimensional databases, metadata, ETL processes, and BI algorithms. The course also covers practical applications of BI in various business contexts. A business intelligence analyst will find the deep dive into business intelligence publisher, and the focus on real-world applications especially beneficial and relevant.

See salaries and explore the career path for Business Intelligence Analyst

Data Analyst

A data analyst collects, processes, and performs statistical analyses on data to identify trends and insights. This course helps those who wish to become a data analyst by exploring data manipulation using Python libraries such as NumPy and Pandas, statistical techniques, and data visualization. The course includes hands-on projects that provide practical experience in applying these techniques. A data analyst will use the foundational knowledge of data types, probability distributions, and hypothesis testing in their daily work.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

A quantitative analyst develops and implements mathematical and statistical models for financial markets. This course may be helpful for a quantitative analyst by covering topics such as statistical modeling, regression analysis, time series analysis, and Bayesian methods. Understanding Monte Carlo simulation, and machine learning techniques such as linear regression will be valuable in the work of a quantitative analyst. The coverage of R programming will be particularly useful.

See salaries and explore the career path for Quantitative Analyst

Machine Learning Consultant

A machine learning consultant advises organizations on how to leverage machine learning technologies to improve their operations. This course helps build a foundation for a machine learning consultant through its broad coverage of machine learning concepts and techniques, as well as practical projects. The machine learning consultant will benefit from the sections on both cloud based machine learning, and the use of machine learning in business. This course will provide a useful background to the work of a machine learning consultant.

See salaries and explore the career path for Machine Learning Consultant

Research Scientist

A research scientist conducts scientific investigations to advance knowledge, often requiring advanced degrees (master's or phd). This course may be useful for a research scientist by covering statistical modeling, machine learning algorithms, and data analysis techniques. The depth of statistical concepts, and the coverage of Bayesian machine learning, will be of particular interest to a research scientist. The hands-on experience with data analysis will also be an asset to the research work of a research scientist.

See salaries and explore the career path for Research Scientist

Financial Analyst

A financial analyst provides guidance to businesses and individuals in making investment decisions. This course may be helpful for a financial analyst, providing an introduction to topics such as prediction, regression analysis, and time series analysis through machine learning techniques. A financial analyst could use these models to identify market trends and make data driven investment proposals. The coverage of business intelligence may also be of interest to a financial analyst.

See salaries and explore the career path for Financial Analyst

Software Developer

A software developer designs, develops, and tests software applications. This course may be helpful for a software developer interested in expanding their skills into the data science field, by covering Python and its libraries, TensorFlow, PySpark, and R. Knowledge of these tools will allow a software developer to work on projects that involve machine learning. A software developer will also be able to learn to create data pipelines to process data effectively through the tools learned in this course.

See salaries and explore the career path for Software Developer

Business Systems Analyst

A business systems analyst assesses the data, processes, and technical aspects of a business. This course may be useful for a business systems analyst by covering business intelligence tools, and techniques used to analyze and improve business processes. The course will introduce a business systems analyst to the concepts of data warehousing, ETL processes, and data visualization. The business systems analyst will find the coverage of business intelligence, and its practical applications helpful.

See salaries and explore the career path for Business Systems Analyst

Statistician

A statistician uses statistical methods to collect, analyze, and interpret data for various purposes, often requiring an advanced degree (master's or phd). This course may be useful for a statistician by covering a broad range of statistical techniques, including data types, probability distributions, hypothesis testing, and various statistical tests within the context of machine learning. A statistician will find that the section on Bayesian machine learning and A/B testing is of particular interest. Knowledge of statistical analysis tools and techniques will be very beneficial in the work of a statistician.

See salaries and explore the career path for Statistician

Project Manager

A project manager plans, oversees, and leads projects from initiation to completion. This course may be helpful for a project manager interested in machine learning projects by covering the project lifecycle as it relates to machine learning. Topics such as meta data and project management can help improve the project planning skills of a project manager. The practical experience of data analysis and machine learning will improve a project manager's understanding of the project lifecycle for data focused projects.

See salaries and explore the career path for Project Manager

Database Administrator

A database administrator manages and maintains the organizational databases to ensure they are secure, functional, and available. This course may be useful for a database administrator, as it touches on topics such as data warehousing, metadata, and ETL processes which are all relevant to the work of a database administrator. The course also discusses the uses of BI tools in enterprise solutions. A database administrator will find this overview of data management concepts to be helpful.

See salaries and explore the career path for Database Administrator

Risk Analyst

A risk analyst assesses and mitigates financial, operational, and strategic risks for an organization. This course may be helpful to a risk analyst by introducing topics such as fraud detection, and machine learning models for prediction and regression, which can be applied to risk modeling. A risk analyst also uses statistical methods to model risks and will find that knowledge of machine learning techniques of particular use in risk analysis.

See salaries and explore the career path for Risk Analyst

Marketing Analyst

A marketing analyst analyzes market trends and consumer behavior to inform marketing strategies. This course may be useful for a marketing analyst by introducing how machine learning can be used to gain insights into consumer behavior, using techniques such as clustering, and regression. Furthermore, topics like data visualization will allow a marketing analyst to present data in a compelling manner for decision making. The skills learned here may assist in the work of a marketing analyst.

See salaries and explore the career path for Marketing Analyst

Machine Learning and Business Intelligence Masterclass

What's inside

Learning objectives

Syllabus

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Reviews summary

Comprehensive machine learning and bi overview

Activities

Career center

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