AI Data Science Engineering - eLearning Bundle CourseNew

Welcome to the AI Data Science Engineering master bundle, your ultimate pathway to becoming a highly skilled professional at the intersection of data science, big data, and artificial intelligence. This comprehensive bundle is meticulously designed for aspiring Data Scientists, Machine Learning Engineers, and Big Data Engineers who want to master Python, deep learning frameworks like TensorFlow and PyTorch, and distributed computing with Apache Spark to build robust AI solutions.

In today's data-driven world, the demand for professionals who can not only analyze data but also engineer scalable AI systems is skyrocketing. This bundle equips you with the foundational knowledge and practical expertise to excel in this dynamic field. You'll move from mastering Python for data science and its crucial libraries (NumPy, Pandas, Matplotlib, Seaborn) to handling and processing big data efficiently using Apache Spark. Dive deep into the core of Artificial Intelligence with extensive modules on Deep Learning, covering neural networks, CNNs, RNNs, and LSTMs using both TensorFlow 2.0 and PyTorch. Learn to build end-to-end machine learning solutions, perform complex data analysis, and implement powerful AI models for various real-world applications including computer vision, natural language processing, and predictive analytics.

What You Will Gain:

Python for Data Science Mastery: Become proficient in Python for data analysis, manipulation, visualization, and machine learning using industry-standard libraries (NumPy, Pandas, Matplotlib, Seaborn).
Big Data Engineering with Apache Spark: Gain in-depth knowledge of Apache Spark (including PySpark) for scalable data processing, real-time analytics, and building robust big data pipelines.
Deep Learning Expertise (TensorFlow & PyTorch): Develop a solid understanding of deep learning concepts and gain hands-on experience building, training, and deploying neural networks (ANN, CNN, RNN, LSTM) using both TensorFlow 2.0 and PyTorch.
End-to-End Machine Learning Solutions: Learn the entire lifecycle of developing and implementing machine learning algorithms, from data cleaning and preparation to model training, evaluation, and prediction.
Real-World AI Applications: Apply deep learning techniques to solve practical problems in diverse domains, including image classification, text generation, stock market prediction, and sentiment analysis.
Data Visualization & Exploration: Master techniques for exploratory data analysis, summarizing data, and creating insightful visualizations to communicate findings effectively.
Distributed Computing Skills: Understand Spark architecture, RDDs, DataFrames, and how to optimize and scale AI/ML workloads on distributed clusters like Hadoop YARN.

Why This Bundle?

The AI Data Science Engineering bundle is designed for ambitious individuals who aspire to roles such as Data Scientist, Machine Learning Engineer, AI Engineer, Big Data Developer, or Data Engineer. If you are looking to combine strong programming skills with deep analytical capabilities and cutting-edge AI knowledge, this bundle offers a structured, project-oriented learning path. Equip yourself with the highly sought-after expertise to design, develop, and deploy intelligent, data-driven solutions in today's rapidly evolving technological landscape.

Accelerate Your AI and Data Engineering Career. Enroll in the AI Data Science Engineering bundle today and build the future of intelligent systems!

Also Included - 4 Courses: An Essential Career Skills Pack with 4 courses in key areas for career management and growth, including Time Management, Digital Skills, Creativity and Soft Skills.

Course Notes

Important Course Information

This is a Lecture-Only eLearning Course. If you would like to practice any hands-on activities, you must use your own software.

Course Objectives:

Master Python for Data Science and Machine Learning:
- Develop proficiency in Python for data manipulation, analysis, visualization, and implementing various machine learning algorithms using libraries like NumPy, Pandas, Matplotlib, and Seaborn.
Design and Implement Big Data Processing Pipelines with Apache Spark:
- Gain expertise in Apache Spark architecture, PySpark, RDDs, DataFrames, and Spark SQL to process, analyze, and scale large datasets in distributed environments.
Build and Deploy Deep Learning Models with TensorFlow and PyTorch:
- Understand fundamental deep learning concepts and acquire hands-on skills in constructing, training, and evaluating artificial neural networks, CNNs, RNNs, and LSTMs for diverse applications like computer vision, NLP, and regression.
Perform End-to-End Data Analysis and Machine Learning Workflows:
- Learn the complete lifecycle of data projects, from data acquisition, cleaning, and preprocessing to exploratory data analysis, model building, and prediction.
Apply Deep Learning to Solve Real-World AI Problems:
- Develop practical skills in using deep learning for tasks such as image classification, text generation, breast cancer classification, stock price prediction, and more.
Optimize and Scale AI/ML Applications:
- Understand techniques for tuning Spark jobs, caching, persisting RDDs, and deploying applications on cloud platforms like Amazon's EMR for efficient distributed computing.
Develop Full-Stack Data Science Applications:
- Integrate data science functionalities with web frameworks like Django to create interactive user interfaces for custom analytic tools.

Target Audience

Aspiring Data Scientists:

Machine Learning Engineers: Professionals seeking to enhance their skills in deep learning frameworks (TensorFlow, PyTorch) and scalable data processing with Spark.
Big Data Engineers/Developers: Those who want to master Apache Spark for building robust data pipelines and integrating AI capabilities.
AI Engineers: Individuals aiming to develop end-to-end AI solutions that involve large datasets and complex deep learning models.
Software Developers: Developers looking to transition into or expand their expertise in data science, machine learning, and AI.
Data Analysts: Professionals seeking to upgrade their analytical skills with programming, big data tools, and advanced machine learning techniques.
Anyone with basic programming knowledge (preferably Python) interested in a comprehensive dive into AI, data science, and big data engineering.

Key Features

Audio Narration
Video
Inline Activities

Exercises
Quizzes
Supplemental Resources

Languages

Audio/Video/Course Text: English.
Subtitles (Closed Caption): N/A.

Course Duration

AI Data Science Engineering: 36 hrs 14 min

Essential Career Skills Pack: 2 hrs 23 min

eLearning Training Delivery Method

Learn at Your Own Pace

This self-paced online course lets you learn independently at your own pace through Certstaffix Training's easy-to-use platform.

How It Works

A Learn at your own pace - Start and stop as it is convenient for you. Pick up where you left off.

Lecture utilizing video and recorded screenshots

6-month subscription length

Instant Access After Purchase

Have more than 10 students needing this course? Contact Us for bulk pricing.

Course Topics

Learning Python for Data Science

Course Duration - 3 hrs 39 min

Python is an open-source, community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to perform data science.

In this course you will learn all the necessary libraries that make data analytics with Python a joy. You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the Numpy library used for numerical and scientific computation. You will also employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. Further you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, and implement your knowledge on projects.

By the end of this course, you will have embarked on a journey from data cleaning and preparation to creating summary tables, from visualization to machine learning and prediction. This course will prepare you to the world of data science.

Course Objectives:

Work with real life data collected from different sources such as CSV files, websites, and databases
Utilize Numpy for numerical and scientific computation
Pre-process data to make it ready for data analysis
Carry out visualization with the Matplotlib and Seaborn libraries
Identify exploratory data analysis, summarizing data, and creating statistics out of data with Pandas
Implement machine learning algorithms and delve into various machine learning techniques, and their advantages and disadvantages
Work with regression, classification, clustering, supervised and unsupervised machine learning

Django with Data Science

Course Duration - 7 hrs 6 min

This course will show you how to create a professional and attractive UI (user interface) in Django for data science using the Semantic UI framework. If you don’t have a programming background, this course will help you create custom analytic tools in the browser by taking you through the core concepts of pandas, NumPy, Matplotlib, and Seaborn. With a step-by-step introduction to new concepts, this Django course will gradually help you get to grips with the essentials of data science.

The code bundle for this course available here.

Course Objectives:

Identify the integration of Django and Python data science libraries: pandas, Matplotlib, Seaborn, and NumPy
Populate the database from CSV files
Define the Semantic UI framework
Identify the basics of data science

Apache Spark 3 for Data Engineering and Analytics with Python

Course Duration - 8 hrs 30 min

Apache Spark 3 is an open-source distributed engine for querying and processing data. This course will provide you with a detailed understanding of PySpark and its stack. This course is carefully developed and designed to guide you through the process of data analytics using Python Spark. The author uses an interactive approach in explaining keys concepts of PySpark such as the Spark architecture, Spark execution, transformations and actions using the structured API, and much more. You will be able to leverage the power of Python, Java, and SQL and put it to use in the Spark ecosystem.

You will start by getting a firm understanding of the Apache Spark architecture and how to set up a Python environment for Spark. Followed by the techniques for collecting, cleaning, and visualizing data by creating dashboards in Databricks. You will learn how to use SQL to interact with DataFrames. The author provides an in-depth review of RDDs and contrasts them with DataFrames.

There are multiple problem challenges provided at intervals in the course so that you get a firm grasp of the concepts taught in the course.

Course Objectives:

Utilize Spark architecture, transformations, and actions using the structured API
Set up your own local PySpark environment
Interpret DAG (Directed Acyclic Graph) for Spark execution
Interpret the Spark web UI
Use the RDD (Resilient Distributed Datasets) API
Visualize (graphs and dashboards) data on Databricks

Pandas for Beginners

Course Duration - 1 hr 56 min

Pandas is one of the most popular Python libraries, used for data analysis and manipulation. It is commonly used in data science, machine learning and artificial intelligence. This comprehensive course introduces you to the basics of data analysis with the Pandas library. You'll begin by working with two primary data structures in Pandas, Series and DataFrame. Then you will see how to read data from a file and explore input data using indexing and filtering. At this point, you are ready to start data preprocessing. You will see how to handle missing values and duplicate rows, and to transform your data into a more efficient format. Next, you'll discover how to manipulate the data and do some processing. Finally, you'll delve into creating simple plots to visualize your data. Upon completion of this course, you will have a firm grasp of one of the most popular, easy to use, open-source Python libraries, enabling you to work with large quantities of data. you'll be able to harness its fast and efficient data manipulation, aggregation, and pivoting, flexible time series functionality, and more. You don't need any previous Pandas experience, but since Pandas is a package built for Python, you need to have a fundamental understanding of basic Python syntax.

Course Objectives:

Install Pandas on your computer
Use the two primary Pandas data structures, Series and DataFrame
View data imported from an external source
Organize input data using indexing and filtering
Use Pandas for data preprocessing
Address missing values and duplicate rows
Format your data most efficiently
Process different data types

Deep Learning with PyTorch

Course Duration - 4 hrs 42 min

This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs.

In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks.

By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems.

All the code and supporting files for this course are available on Github at here.

This course uses Python 3.6, and PyTorch 0.3, while not the latest version available, it provides relevant and informative content for legacy users of Python , and PyTorch.

Course Objectives:

Define PyTorch and deep learning concepts
Build your neural network using deep learning techniques in PyTorch
Perform basic operations on your dataset using tensors and variables
Build artificial neural networks in Python with GPU acceleration
Identify how CNN works in PyTorch with a simple computer vision example
Train your RNN model from scratch for text generation
Use Auto Encoders in PyTorch to remove noise from images
Perform reinforcement learning to solve OpenAI's Cartpole task and extend your knowledge of deep learning by using PyTorch to solve your own machine learning problems

Implementing Deep Learning Algorithms with TensorFlow 2.0

Course Duration - 2 hrs 16 min

Deep learning has caused the revival of artificial intelligence. It has become the dominant method for speech recognition (Google Assistant), computer vision (search for "my pictures" on Google Photos), language translation, and even game-related Artificial Intelligence (think AlphaGo and DeepMind). If you'd like to learn how these systems work and maybe make your own, deep learning is for you!

In this course, you will gain a solid understanding of deep learning models and use deep learning techniques to solve business and other real-world problems to make predictions quickly and easily. You will learn various deep learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2.0. You will program a model to classify breast cancer, predict stock market prices, process text as part of Natural Language Processing (NLP), and more.

By the end of this course, you will have a complete understanding to use the power of TensorFlow 2.0 to train deep learning models of varying complexities.

The code bundle and supporting files for this course are available on GitHub here.

Course Objectives:

Define what deep learning and TensorFlow 2.0 are and what problems they have solved and can solve
Identify the various deep learning model architectures and work with them
Apply neural network models, deep learning, NLP, and LSTM to several diverse data classification scenarios, including breast cancer classification
Predict stock market data for Google
Classify Reuters news topics, and classifying flower species
Apply your newly-acquired skills to a wide array of practical and real-world scenarios

Apache Spark with Python: Big Data with PySpark and Spark

Course Duration - 3 hrs 18 min

This course covers all the fundamentals of Apache Spark with Python and teaches you everything you need to know about developing Spark applications using PySpark, the Python API for Spark. At the end of this course, you will gain in-depth knowledge about Apache Spark and general big data analysis and manipulations skills to help your company to adopt Apache Spark for building big data processing pipeline and data analytics applications. This course covers 10+ hands-on big data examples. You will learn valuable knowledge about how to frame data analysis problems as Spark problems. Together we will learn examples such as aggregating NASA Apache weblogs from different sources; we will explore the price trend by looking at the real estate data in California; we will write Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data; and we will develop a system to analyze how maker spaces are distributed across different regions in the United Kingdom.

Course Objectives:

Define the architecture of Apache Spark
Develop Apache Spark 2.0 applications using RDD transformations and actions and Spark SQL
Work with Apache Spark's primary abstraction, resilient distributed datasets (RDDs) to process and analyze large data sets
Analyze structured and semi-structured data using DataFrames, and develop a thorough understanding about Spark SQL
Optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs
Scale up Spark applications on a Hadoop YARN cluster through Amazon's Elastic MapReduce service
Share information across different nodes on an Apache Spark cluster by broadcast variables and accumulators
Write Spark applications using the Python API - PySpark

Deep Learning: Artificial Neural Networks with TensorFlow

Course Duration - 4 hrs 47 min

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

Course Objectives:

Define what machine learning is
Build linear models with TensorFlow 2
Build deep neural networks with TensorFlow 2
Perform image classification and regression with ANN
Explain loss functions such as mean-squared error and cross-entropy loss
Define stochastic gradient descent, momentum, and Adam optimization

Essential Career Skills Pack

Productivity and Time Management

Course Duration - 30 min

It seems that there is never enough time in the day. But, since we all get the same 24 hours, why is it that some people achieve so much more with their time than others? This course will explain how to plan and prioritize tasks, so that we can make the most of the limited time we have. By using the time-management techniques in this course, you can improve your ability to function more effectively – even when time is tight and pressures are high. So, by the end of the course you will have the knowledge, skills and confidence to be an effective manager of your time.

Course Objectives:

Set your priorities to better manage your time
Improve your productivity by sharpening your focus and multitasking effectively

Detailed Course Outline:

Productiity & Time Management
Prioritization
Getting Things Done

Procrastination
Multitasking & Focus
Summary

Basic Digital Skills

Course Duration - 13 min

With the rise of digital transformation and technology, having a basic digital literacy is essential for all types of jobs, regardless of the industry. To stay competitive and be successful in the workplace, enhancing your digital skills should be a top priority.

Course Objectives:

Recall the essential digital skills framework
Elaborate on the toolkit of essential digital skills
Identify how to develop or improve your digital skills

Detailed Course Outline:

The Essential Digital Skills Framework
The Toolkit of Essential Digital Skills

Developing Digital Skills
Summary

4 Ways to Boost Creativity

Course Duration - 30 min

The digital economy is opening up ways for everyone to be creative. It doesn’t just mean being artistic – it’s more about ideas, solutions, alternatives, incremental improvements. Peter Quarry and Eve Ash discuss ways that mental capacity can be developed, perspectives changed, group power leveraged and making things actually happen.

Course Objectives:

Define creativity
Think outside the box
Develop the right mental attitude
Leverage the power of groups
Ensure managers make it happen

Detailed Course Outline:

What is Creativity at Work?
Learn to Think Outside the box
Develop the Right Mental Capacity

Laverage the Power of Groups
Ensure Managers Make It Happen
Summary

The 11 Essential Career Soft Skills

Course Duration - 1 hr 10 min

Soft Skills are the traits, characteristics, habits, and skills needed to survive and thrive in the modern work world. Soft skills aren't usually taught in school, but you will learn them all here in this course. Are you someone that other people in your organization and industry like to work with, collaborate with and partner with? Are you seen as a valuable asset to any new project that comes along?

This soft skills training course will teach you how to develop the skills that can make the difference between a lackluster career that tops out at middle management versus one that lands you in the executive suite. Or to wherever you define career success. So many soft skills seem like common sense at first glance, but they are not commonly applied by most workers. This soft skills training course will give you an edge over your competitors. It will also make your job, your career and your life more rewarding and enjoyable.

Course Objectives:

Understand how to be a great communicator
Become a stronger listene
Appear professional to co-workers and bosses of all ages
Avoid common career blunders that often end careers
Manage expectations for bosses and colleagues
Position yourself for promotions
Make technology your asset, even if you are afraid of technology
Avoid the Not My Job Syndrome
Develop EQ to Match Your IQ
Develop leadership qualities

Detailed Course Outline:

Introduction
The Soft Tech Savvy Way to Always Be Essential
Not My Job, And I Am Happy to Do It
You Can Become a Master Communicator
Feedback Video for The 11 Essential Career Soft Skills
Become a Leader Without the Title or Formal Authority

Your EQ Will Beat a Higher IQ
Building Your Winning Team
Make Every One of Your Seconds Count
Unleash Your Inner Anthony Robbins
Avoid Being Uncool
Clothes Can Still Make or Break Your Career
Conclusion The 11 Essential Career Soft Skills
Extra: Developing Your Career Secret Sauce

Related Generative AI & ChatGPT Information:

How Much Do AI Training Courses Cost?

Public instructor-led AI course prices start at $460 per student. Group training discounts are available.

Self-Paced AI eLearning courses cost $475 at the starting point per student. Group purchase discounts are available.

What Generative AI Skills Should I Learn?

A: If you are wondering what Generative AI skills are important to learn, we've written a Generative AI Skills and Learning Guide that maps out Generative AI skills that are key to master and which of our courses teaches each skill.

Read Our Generative AI Skills and Learning Guide

How Can I Learn Generative AI?

Learning Generative AI involves understanding the foundational concepts of artificial intelligence, machine learning, and deep learning, followed by hands-on practice with relevant tools and frameworks.

Certstaffix Training offers three efficient methods to pursue your Generative AI training: Engaging Live Online Classes, Tailored Onsite Corporate Training for teams and Self-Paced eLearning.

View our Generative AI courses and available training methods.

Is It Hard to Learn Generative AI?

Learning Generative AI can vary in difficulty depending on your background and prior knowledge of related concepts.

For individuals with experience in computer science, programming, or machine learning, the process may be more straightforward as it builds upon foundational principles in these areas. For beginners, there can be a steeper learning curve since understanding Generative AI requires grasping concepts like neural networks, data preprocessing, and algorithm training.

However, with the right resources, such as Certstaffix structured courses, hands-on practice, and guidance from experienced instructors, anyone can gradually develop a strong understanding of Generative AI. Like any skill, persistence and consistent practice are key to mastering it.

Does Generative AI Require Coding?

The need for coding in Generative AI largely depends on the tools and platforms being used, as well as the user's goals. Modern Generative AI platforms, such as ChatGPT or DALL·E, often provide user-friendly interfaces that require little to no coding knowledge. These tools enable users to generate content, images, or text through simple commands or prompts.

However, for those looking to customize or build their own Generative AI models, coding is typically required. This may involve knowledge of programming languages like Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch. Understanding coding can also help in fine-tuning models, integrating AI into larger systems, or creating uniquely tailored applications.

Bottom line, while coding can enhance the scope of possibilities, it is not usually necessary for leveraging Generative AI's capabilities as an end user.

Can Anybody Learn AI?

Absolutely, anyone with interest and dedication can learn AI. While having a background in mathematics, programming, or data science can be advantageous, they are not strict prerequisites. Numerous online courses, tutorials, and resources are available to cater to learners of all levels, from beginners to advanced practitioners. The key lies in starting with fundamental concepts, such as understanding algorithms and machine learning basics, and gradually advancing to more complex topics. With consistent effort, anyone can develop the skills required to explore and contribute to the exciting field of AI.

Is it Worth Doing an AI Course?

Deciding whether to pursue an AI course ultimately depends on your goals, interests, and career aspirations. AI is one of the fastest-growing fields, with applications spanning industries such as healthcare, finance, entertainment, and more. Taking a course can be a valuable investment, equipping you with the knowledge and skills to tap into these opportunities. Furthermore, structured courses often provide hands-on projects, mentorship, and certifications that can enhance your resume and increase your credibility in the job market.

However, it’s important to assess the quality of the course and its relevance to your objectives. A well-structured AI course should provide a balance of theoretical knowledge, like understanding algorithms or neural networks, and practical experience with tools such as TensorFlow or PyTorch. Whether you aim to transition into a career in AI, enhance your current role, or simply satiate your curiosity, an AI course can offer significant value when aligned with your ambitions and commitment to learning.

How Is AI Used in Excel?

A: Many of Excel's features and functions are powered by artificial intelligence (AI) or have AI-like capabilities. These tools work to process large amounts of data for users, streamline workflows, and increase accuracy. Some of the best applications of AI in Excel include data analysis, data visualization, and formula generation, but it can also supercharge basic functions. Copilot allows users to harness the power of AI through prompts to provide relevant suggestions and shortcuts. Additionally, ChatGPT can be integrated with the program through the use of an add-on or used in tandem with Excel.

More Information on How Excel Integrates With AI

Where Can I Learn More About AI?

AI Blogs

AI Groups

AI Online Forums

Explore AI Classes Near Me:

Certstaffix Training provides AI classes near me or online, depending on the number of students involved. We offer online courses for individual learners, as well as in person classes at your office for corporate groups. Our trainers are highly experienced professionals with the expertise necessary to help you gain a thorough understanding of AI concepts and tools. With our courses available online for individuals or in person for corporate groups, it's easy to develop your AI skills. Start learning today and see how Certstaffix Training can help you reach your goals.

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