Master Machine Learning - eLearning Bundle CourseNew

Are you ready to transform your career and become a leader in the rapidly evolving field of Artificial Intelligence and Machine Learning? The Master Machine Learning - eLearning Bundle is your comprehensive pathway to mastering cutting-edge machine learning concepts, from foundational principles to advanced practical applications. Designed for aspiring data scientists, machine learning engineers, software developers, and IT professionals, this bundle equips you with the in-demand skills to build, deploy, and optimize intelligent systems.

What You'll Gain:

Comprehensive Skill Development: Dive deep into core machine learning algorithms, including supervised and unsupervised learning, regression, classification, and clustering.
Hands-On Practical Experience: Build a robust portfolio of real-world projects using industry-standard tools and libraries like Python, Scikit-Learn, Keras, Matplotlib, Seaborn, and Pandas.
Cloud-Agnostic Expertise: Learn to leverage powerful cloud platforms such as Amazon AWS (Redshift ML, SageMaker), Microsoft Azure Machine Learning, and Google Cloud for scalable ML solutions.
Problem-Solving Mastery: Develop the ability to identify, analyze, and solve complex business problems using machine learning, from building recommender systems to performing sentiment analysis and image recognition.
Best Practices and Ethical Considerations: Understand crucial aspects of data privacy, risk management, model explainability, and ethical AI development to ensure responsible and effective deployment.
Career Advancement: Position yourself for lucrative opportunities in data science, machine learning engineering, and AI development by acquiring a highly sought-after skill set.

This bundle emphasizes practical application, guiding you through the entire machine learning lifecycle—from data preparation and model training to deployment and optimization. Whether you're a beginner looking to establish a strong foundation or an experienced professional aiming to enhance your expertise, the Master Machine Learning - eLearning Bundle provides the knowledge and tools you need to excel.

Ready to build the future with AI? Enroll in the Master Machine Learning - eLearning Bundle today and start your journey to becoming a machine learning expert!

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 Core Machine Learning Concepts:
- Explain the basics of AI, ML, and Deep Learning concepts.
- Define machine learning, linear regression, classification, and supervised/unsupervised learning.
- Understand the various regression, classification, and other ML algorithm performance metrics (e.g., R-squared, MSE, accuracy, confusion matrix, precision, recall) and when to use them.
- Identify the different types of ML approaches and the models within each section, and describe how each model works.
- Explore neural networks and their applications, including building larger models for image and text data.
- Understand the intuition behind contemporary machine learning models and algorithms.
Develop Practical Machine Learning Skills with Key Libraries and Tools:
- Set up a Python development environment correctly, including Anaconda and Jupyter.
- Gain complete machine learning toolsets to tackle most real-world problems using Python, Scikit-Learn, Keras, Matplotlib, Seaborn, and Pandas.
- Format, load, prepare, clean, group, sort, export, select, and filter data for machine learning algorithms.
- Create linear regressions, decision trees, and various charts (pie, treemap, swarm, histogram, density, box, whisker, bar, grouped bar, stacked bar, lollipop, scatter, correlogram, line, area) for data visualization and exploratory data analysis (EDA).
- Build end-to-end regression and classification models, and develop and evaluate neural network models.
- Implement feature selection and extraction for dimensionality reduction.
- Use checkpointing to save the best model run and perform cross-validation.
- Engineer new features to improve algorithm predictions and combine multiple models by bagging, boosting, or stacking.
Build and Deploy Machine Learning Solutions on Cloud Platforms:
- Utilize AWS machine learning services, including AWS SageMaker, Redshift ML, for building and deploying machine learning models.
- Work with Azure ML Studio and create machine learning experiments, modeling real business use cases.
- Design, implement, and manage complex AI/ML workloads on Google Cloud, including building, training, and deploying ML models.
- Create a SageMaker endpoint and use it to build a Redshift ML model for remote inference.
- Operationalize machine learning in your data warehouse.
Address Real-World Machine Learning Challenges:
- Build powerful machine learning models to solve any problem, including recommender systems, sentiment prediction, document classification, image recognition, product recommender systems, and price predictions.
- Identify common pitfalls and mistakes to avoid when building machine learning models, such as dealing with bad data, preventing overfitting, and avoiding imbalanced sampling.
- Understand and apply privacy-preserving machine learning techniques, including differential privacy, federated learning, and secure multiparty computation.
- Implement best practices in machine learning development and deployment, including data provisioning, processing, quality control, testing, and validation.
- Gain an understanding of AI risk management frameworks and techniques.
- Dive into generative AI with use cases, architecture patterns, and Retrieval Augmented Generation (RAG).

Target Audience

Aspiring Data Scientists and Machine Learning Engineers: Individuals looking to build a strong foundation and practical skills in machine learning for a career in data science or ML engineering.
Software Developers and IT Professionals: Developers and IT administrators who want to integrate machine learning capabilities into their applications or manage ML infrastructure.
Data Analysts and Business Intelligence Professionals: Professionals seeking to advance their analytical skills by incorporating machine learning techniques for deeper insights and predictive modeling.
Researchers and Academics: Those interested in understanding the practical applications and methodologies of machine learning.
Anyone with a basic understanding of programming (preferably Python) and foundational mathematical concepts (basic calculus, statistics) who is eager to enter or advance in the field of Artificial Intelligence and Machine Learning.

Key Features

Audio Narration
Video
Inline Activities

Exercises
Quizzes
Supplemental Resources

Languages

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

Course Duration

Master Machine Learning: 146 hrs 9 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

Machine Learning with Scikit-Learn

Course Duration - 45 min

Machine learning is an exciting and rapidly growing concept. In this course, renowned python instructor and blogger Michael Galarnyk, will demonstrate effective machine learning techniques using one of the best machine learning libraries available: Scikit-Learn. you'll gain an understanding of how machine learning works as well as how to input and load data. Michael will share how to manage supervised learning, including the creation of supervised learning through a variety of techniques. Then, you'll explore unsupervised learning and methodologies for improving your learning algorithms. By the end of this course, you'll feel comfortable training your first model in Scikit-Learn.

Course Objectives:

Define machine learning
Explore the real value of Scikit-Learn
Format data
Create linear regressions
Create decision trees
Decide on a learning model
Manage supervised or unsupervised learning
Create visualizations

Mistakes to Avoid in Machine Learning

Course Duration - 40 min

It's exciting to build machine learning models, but dealing with errors, bad output or a host of other issues can slow your progress. In this fast-paced course from Patagonia data scientist Brett Vanderblock, you'll learn the mistakes you should avoid when building machine learning models. you'll explore how to better work with experts, standardize your data, prevent data leakage, and how to give better presentations. From dealing with bad data, to preventing overfitting, to not getting feedback, you'll know how to avoid key mistakes when building your machine learning models.

Course Objectives:

Improve your models
Prevent data leakage
Deploy your models more effectively
Evaluate your models accurately
Incorporate simple statistics methods
Determine if machine learning is necessary for your use case
Avoid imbalanced sampling

The Complete Machine Learning Course with Python

Course Duration - 18 hrs 22 min

Do you want to be a data scientist and build machine learning projects that can solve real-life problems? If yes, then this course is perfect for you. During the course, you will learn how to: Set up a Python development environment correctly Gain complete machine learning toolsets to tackle most real-world problems Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, and when to use them Combine multiple models with by bagging, boosting, or stacking Make use of unsupervised machine learning algorithms such as Hierarchical clustering and k-means clustering to understand your data Develop in Jupyter (IPython) notebook, Spyder and various IDE Communicate visually and effectively with Matplotlib and Seaborn Engineer new features to improve algorithm predictions Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data Use SVM for handwriting recognition, and classification problems in general Use decision trees to predict staff attrition Apply the association rule to retail shopping datasets By the end of this course, you will have a Portfolio of 12 machine learning projects that will help you land your dream job or enable you to solve real-life problems in your job or personal life with machine learning algorithms.

Course Objectives:

Build powerful machine learning models to solve any problem

Hands-On Scikit-learn for Machine Learning

Course Duration - 9 hrs 3 min

Scikit-learn is arguably the most popular Python library for machine learning today. Thousands of data scientists and machine learning practitioners use it for day to day tasks throughout a machine learning project’s life cycle. Due to its popularity and coverage of a wide variety of machine learning models and built-in utilities, jobs for Scikit-learn are in high demand, both in industry and academia. If you’re an aspiring machine learning engineer, ready to take real-world projects head-on, Hands-on Scikit-Learn for machine learning will walk you through the most commonly used models, libraries, and utilities offered by Scikit-learn. By the end of the course, you will have a set of machine learning problem-solving tools in the form of code modules and utility functions based on Scikit-learn in one place, instead of spread over several books and courses, which you can easily use on real-world projects and data sets.

Course Objectives:

Tackle real-world problems in machine learning through a structured process using Scikit-learn
Achieve substantially more in less time and with much less code by leveraging the power and simplicity of Scikit-learn
Develop a thorough understanding of core predictive analytics with regression, classification, and unsupervised learning such as clustering and PCA
Create ensemble models with Random-Forest and Gradient-boosting methods and see your model performance improve drastically
Build a portfolio of tools and techniques that can readily be applied to your own projects
Identify the intuition behind contemporary machine learning models and algorithms without going into deep mathematical details
Evaluate and improve the accuracy and performance of machine learning models
Identify the foundations of text analytics and develop a set of tools to apply to your common text-analysis tasks

Fundamentals of Machine Learning

Course Duration - 8 hrs 41 min

This is an introductory course on machine learning. The course covers a wide range of topics, from handling a dataset to model delivery. Some prior training in Python programming and basic calculus knowledge will help you get the best out of this course.

Course Objectives:

Explain the basics of statistical learning
Define linear regression, classification, and supervised learning
Describe sampling and Bootstrap in machine learning
Explain model selection and regularization
Define random forests and decision trees
Explain labs on Multilayer Perceptron (MLP)?and RNN

Machine Learning for Absolute Beginners: Level 1

Course Duration - 4 hrs 27 min

Learn the fundamentals of Machine Learning with a focus on clarity and simplicity. This course unpacks AI's rise, ML classifications, and the transformative impact of generative AI. Perfect for beginners, it provides a solid grounding for advancing into more complex AI topics.

Course Objectives:

Explain the basics of AI, ML, and Deep Learning concepts
Identify key Machine Learning system classifications
Describe how ML models are trained and generalized
Explore neural networks and their applications
Recognize challenges like bias and prompt sensitivity in AI
Apply ML concepts to real-world AI use cases effectively

Machine Learning for Absolute Beginners: Level 2

Course Duration - 3 hrs 59 min

Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library. The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms. By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

Course Objectives:

Use Jupyter Lab for Jupyter notebooks
Explain Python fundamentals
Load large datasets from files using Pandas
Identify techniques to perform data analysis and exploration
Group, sort, export, select, filter, and clean data
Preview the data frame

Machine Learning for Absolute Beginners: Level 3

Course Duration - 2 hrs 59 min

In the first and second course of the “Machine Learning for Absolute Beginners” training program, you have learned the fundamentals of AI and machine learning and discovered methods to pre-process the data before moving it into the machine learning algorithms. In this third and final course of the program, you will learn how to create eye-catching data visualizations using Python, Seaborn, and Matplotlib. The course starts by highlighting the learning objectives and then takes you through the fundamentals of Matplotlib and Seaborn. You will learn how to use figures, axes, customization techniques, and NumPy to perform data visualization. In the rest of this course, you will discover how to develop the ranking, proportion, trend, distribution, and correlation charts. By the end of this course, you will have the knowledge and skills to perform data visualization and exploratory data analysis (EDA) using Python, Matplotlib, and Seaborn.

Course Objectives:

Utilize object-oriented and Pyplot interfaces of Matplotlib
Identify Seaborn and figure-level and axes-level functions
Create pie, treemap, and swarm charts
Plot histogram, density, box, and whisker charts
Create bar, grouped bar, stacked bar, and lollipop charts
Create scatter, correlogram, line, and area charts

Python Machine Learning Bootcamp

Course Duration - 23 hrs 59 min

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Course Objectives:

Determine how to take an ML idea and flush it out into a fully functioning project
Identify the different types of ML approaches and the models within each section
Describe how each model works
See the practical application and implementation for each model we cover
Determine how to optimize models
Identify the common pitfalls and how to overcome them

Building Recommender Systems with Machine Learning and AI

Course Duration - 11 hrs 24 min

Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you would like to watch? Are you looking for a practical course that will teach you how to build intelligent recommendation systems? This course will show you how to build accurate recommendation systems in Python using real-world examples.

Course Objectives:

Provide a basic overview of the architecture of recommender systems
Test and evaluate recommendation algorithms with Python
Use K-Nearest-Neighbors to recommend items to users
Find solutions to common issues with large-scale recommender systems
Make session-based recommendations with recurrent neural networks
Use Apache Spark to compute recommendations at a large scale on a cluster

Recommender Systems with Machine Learning

Course Duration - 6 hrs 17 min

The course is crafted to help you understand not only the role and impact of recommender systems in real-world applications but also provide hands-on experience in developing complete recommender systems engines for your customized dataset using projects. This learning-by-doing course will help you master the concepts and methodology of Python.

Course Objectives:

Explain AI-integrated recommender systems basics
Identify the basic taxonomy of recommender systems
Determine the impact of overfitting, underfitting, bias, and variance
Build content-based recommender systems with ML and Python
Build item-based recommender systems using ML techniques and Python
Model KNN-based recommender engine for applications

Machine Learning Projects with Java

Course Duration - 2 hrs 12 min

In this course, you will learn how to build a model that takes complex feature vector form sensor data and classifies data points into classes with similar characteristics. Then you will predict the price of a house based on historical data. Finally, you will build a Deep Learning model that can guess personality traits using labeled data.

Course Objectives:

Perform classification using the Weka Library
Implement Pattern Recognition of non-labeled data
Build Regression models for data with multiple features
Save trained models for further reusability
Perform cross-validation
Leverage Deep Learning in ML problems
Implement Natural Language Processing with Deep Learning

Hands-On Keras for Machine Learning Engineers

Course Duration - 2 hrs 17 min

Welcome to hands-on Keras for machine learning engineers. This is a carefully structured course to guide you in your journey to learn deep learning in Python with Keras. Discover the Keras Python library for deep learning and learn the process of developing and evaluating deep learning models using it. There are two top numerical platforms for developing deep learning models; they are Theano, developed by the University of Montreal, and TensorFlow developed at Google. Both were developed for use in Python and both can be leveraged by the super-simple-to-use Keras library. Keras wraps the numerical computing complexity of Theano and TensorFlow, providing a concise API that we will use to develop our own neural network and deep learning models. Keras has become the gold standard in the applied space for rapid prototyping deep learning models. This course is a hands-on guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to your own deep learning Keras models.

Course Objectives:

Develop and evaluate neural network models end-to-end
Build larger models for image and text data
Define the anatomy of a Keras model
Evaluate the performance of a deep learning Keras model
Build end-to-end regression and classification models in Keras
Use checkpointing to save the best model run
Explore what's in a database whenever you want
Create basic reports with the data from your database

Privacy-Preserving Machine Learning

Course Duration - 6 hrs 42 min

This ebook helps software engineers, data scientists, ML and AI engineers, and research and development teams to learn and implement privacy-preserving machine learning as well as protect companies against privacy breaches.

Course Objectives:

Study data privacy, threats, and attacks across different machine learning phases
Describe Uber and Apple cases for applying differential privacy and enhancing data security
Identify IID and non-IID data sets as well as data categories
Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks
Identify secure multiparty computation with PSI for large data
Describe confidential computation and find out how it helps data in memory attacks

Serverless Machine Learning with Amazon Redshift ML

Course Duration - 4 hrs 50 min

Machine learning pipelines are expensive and complex, requiring industries to enable their data science teams with the ability to train models and run predictions with easy-to-use tools. This ebook helps you implement end-to-end serverless architectures for ingestion, analytics, and machine learning using Redshift Serverless and Redshift ML.

Course Objectives:

Utilize Redshift Serverless for data ingestion, data analysis, and machine learning
Create supervised and unsupervised models and supply your own custom parameters
Use time series forecasting in your data warehouse
Create a SageMaker endpoint and use that to build a Redshift ML model for remote inference
Determine how to operationalize machine learning in your data warehouse
Use model explainability and calculate probabilities with Amazon Redshift ML

The Machine Learning Solutions Architect Handbook

Course Duration - 10 hrs 2 min

This ebook emphasizes AI risk management, the AI/ML adoption journey, and the emerging field of generative AI, highlighting the increasing importance of developing secure and scalable ML platforms across industries.

Course Objectives:

Apply ML methodologies to solve business problems across industries
Design a practical enterprise ML platform architecture
Gain an understanding of AI risk management frameworks and techniques
Build an end-to-end data management architecture using AWS
Train large-scale ML models and optimize model inference latency
Create a business application using artificial intelligence services and custom models
Dive into generative AI with use cases, architecture patterns, and RAG

Machine Learning Infrastructure and Best Practices for Software Engineers

Course Duration - 5 hrs 46 min

Machine learning is an important driver of innovation in software products. This ebook will help you take your machine learning prototype to the next level and scale it up using concepts such as data provisioning, processing, and quality control.

Course Objectives:

Identify what the machine learning software best suits your needs
Work with scalable machine learning pipelines
Scale up pipelines from prototypes to fully fledged software
Choose suitable data sources and processing methods for your product
Differentiate raw data from complex processing, noting their advantages
Track and mitigate important ethical risks in machine learning software
Work with testing and validation for machine learning systems

MATLAB for Machine Learning

Course Duration - 6 hrs 14 min

Unlock the power of MATLAB for machine learning with this comprehensive ebook. From data handling to advanced techniques like deep learning, NLP, and anomaly detection, gain practical skills applicable across a range of AI applications.

Course Objectives:

Discover different ways to transform data into valuable insights
Explore the different types of regression techniques
Describe the basics of classification through Naive Bayes and decision trees
Use clustering to group data based on similarity measures
Perform data fitting, pattern recognition, and cluster analysis
Implement feature selection and extraction for dimensionality reduction
Harness MATLAB tools for deep learning exploration

Hands-On Machine Learning for .NET Developers

Course Duration - 2 hrs 47 min

ML.NET enables developers utilize their .NET skills to easily integrate machine learning into virtually any .NET application. This course will teach you how to implement machine learning and build models using Microsoft's new Machine Learning library, ML.NET. You will learn how to leverage the library effectively to build and integrate machine learning into your .NET applications. By taking this course, you will learn how to implement various machine learning tasks and algorithms using the ML.NET library, and use the Model Builder and CLI to build custom models using AutoML. You will load and prepare data to train and evaluate a model; make predictions with a trained model; and, crucially, retrain it. You will cover image classification, sentiment analysis, recommendation engines, and more! You'll also work through techniques to improve model performance and accuracy, and extend ML.NET by leveraging pre-trained TensorFlow models using transfer learning in your ML.NET application and some advanced techniques. By the end of the course, even if you previously lacked existing machine learning knowledge, you will be confident enough to perform machine learning tasks and build custom ML models using the ML.NET library.

Course Objectives:

Implement machine learning algorithms directly within your current cross-platform .Net applications, such as ASP.Net Web.APIs, desktop applications, and Dotnet core console apps
Use the advances in machine learning with models customized to your needs
Automatically evaluate different machine learning models fast using AutoML, Model Builder, and CLI tools
Improve and retrain your models for better performance and accuracy
Use different machine learning algorithms to solve problems such as sentiment prediction, document classification, image recognition, product recommender systems, price predictions, and Bitcoin price forecasting
Load data and prepare for model training
Leverage state of the art TensorFlow and ONNX models directly in .NET

Machine Learning with Jupyter Notebooks in Amazon AWS

Course Duration - 5 hrs

Unlock the potential of artificial intelligence and machine learning with this comprehensive course tailored for IT administrators, data center architects, consultants, enterprise architects, programmers, data security specialists, and big data analysts. Whether you're seeking fundamental or intermediate level skills, this course provides a fascinating journey into the world of machine learning, empowering you to excel in your career and enjoy lucrative opportunities. Through a blend of theory and hands-on practice, participants will delve into essential machine learning topics, work with Jupyter Notebooks, explore reinforcement learning concepts, and leverage machine learning services in AWS, including AWS SageMaker. The course covers dynamic programming, Q-learning, best practices, and much more, providing practical insights and techniques to take your skills to the next level. To enroll in this course, participants should meet the following requirements: Basic knowledge of AWS services is beneficial. A valid AWS account is required for hands-on practice (a credit card is required to open an AWS account).

Course Objectives:

Explain machine learning fundamentals and their applications
Use Jupyter Notebooks for data analysis and machine learning experiments
Apply reinforcement learning concepts and their practical implementations
Utilize AWS machine learning services, including AWS SageMaker, for building and deploying machine learning models
Describe dynamic programming and Q-learning algorithms for reinforcement learning tasks
Implement best practices in machine learning development and deployment

Azure Machine Learning Basics

Course Duration - 1 hr 30 min

This unique course offers a comprehensive introduction to Microsoft Azure Machine Learning (ML) fundamentals, tailored for beginners seeking to gain essential skills and embark on a rewarding career in data science and machine learning. Using a flipped classroom model with hands-on learning, participants will dive directly into the course content, guided by practical exercises and real-world examples. Throughout the course, participants will work in Azure ML Studio, create machine learning experiments, and model real business use cases. They will learn basic concepts of machine learning, understand best practices, and gain practical experience in leveraging Azure ML for various tasks. To enroll in this course, participants should meet the following requirements: A credit card is required to create a Microsoft Azure account for hands-on practice. Knowledge of basic statistical concepts is highly desirable but not required.

Course Objectives:

Work with Azure ML Studio and creating machine learning experiments
Model real business use cases using Azure ML capabilities
Define basic machine learning concepts and principles
Apply best practices for implementing machine learning solutions in Azure

Google Machine Learning and Generative AI for Solutions Architects

Course Duration - 9 hrs 12 min

Whether you are relatively new to AI/ML or an experienced solutions architect working to address emerging business challenges, this ebook covers everything you need to design, implement, and manage complex AI/ML workloads on Google Cloud. Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark Source and prepare data for ML workloads Build, train, and deploy ML models on Google Cloud Create an effective MLOps strategy and implement MLOps workloads on Google Cloud Discover common challenges in typical AI/ML projects and get solutions from experts Identify vector databases and their importance in Generative AI applications Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows

Course Objectives:

Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark
Source and prepare data for ML workloads
Build, train, and deploy ML models on Google Cloud
Create an effective MLOps strategy and implement MLOps workloads on Google Cloud
Discover common challenges in typical AI/ML projects and get solutions from experts
Identify vector databases and their importance in Generative AI applications
Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows

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

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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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