Welcome to 'Data Scientist - Job training', a first-of-its kind short program designed for jobseekers and career change aspirants.
This course is specifically created as a JOB-BASED TRAINING program thereby teaching concepts hands-on and relevant to real-work environment. If you are looking for a job in Data Science or if you are a student who would like to get first experience in this domain, this course is exactly for you.
How is this program a JOB-BASED TRAINING and how will it equip you with skills relevant for your role:
What companies ask for a Data Scientist role?
Gather and clean large datasets from diverse sources to ensure data accuracy and completeness.
Collaborate with cross-functional teams to understand data requirements and optimize data collection processes.
Conduct exploratory data analysis to identify patterns, trends, and anomalies.
Design and implement machine learning models for predictive and prescriptive analytics.
Develop and engineer relevant features to improve model performance and accuracy.
Evaluate model performances
Stay informed about the latest advancements in machine learning and data science techniques.
How this course meets the requirements?
Learn about Data Loading and EDA
Collaborate with team members on various tasks and on Kaggle
Gain knowledge on EDA and Data Insight generation
Understand the concepts of feature engineering, feature selection, baseline model building, model performance analysis and model metrics
Learn about hyperparameter tuning, comparison of models, grid search, cross-validation
Learn and evaluate model performance and compare various models
Get introduced to NLP and advancements in Data Science
Tools you will learn:
Pandas
Numpy functions
Python functions
Kaggle notebooks
Google colab
matplotlib
seaborn
nltk
Scikit learn
XGBoost
LightGBM
Transformers, and many more ML packages and libraries, model metrics: cross entropy, DB Index, etc. NLP, and LLMs for ML tasks (hugging face library)
Extra Module and Benefits:
1. AI Fundamentals and Applications:
Unlock exclusive access to one of our AI modules Learn from our experts leveraging AI to enhance your productivity and understand the wide variety of applications of AI across industries
2. Career Guidance
Understand how to effectively search for a job, find startups, craft a compelling CV and Cover Letter, types of job platforms and many more!
Trainers:
Dr. Chetana Didugu - Germany
Dr. Chetana Didugu is an Experienced Data Scientist, Product Expert, and PhD graduate from IIM Ahmedabad. She has worked 10+ years in various top companies in the world like Amazon, FLIX, Zalando, HCL, etc in topics like Data Analysis and Visualisation, Business Analysis, Product Management, Product Analytics & Data Science. She has trained more than 100 students in this domain till date.
Aravinth Palaniswamy - Germany
Founder of 2 startups in Germany and India, Technology Consultant, and Chief Product Officer of Moyyn, and has 10+ years of experience in Venture Building, Product and Growth Marketing.
Welcome to 'Data Scientist - Job training', a first-of-its kind short program designed for jobseekers and career change aspirants.
This course is specifically created as a JOB-BASED TRAINING program thereby teaching concepts hands-on and relevant to real-work environment. If you are looking for a job in Data Science or if you are a student who would like to get first experience in this domain, this course is exactly for you.
How is this program a JOB-BASED TRAINING and how will it equip you with skills relevant for your role:
What companies ask for a Data Scientist role?
Gather and clean large datasets from diverse sources to ensure data accuracy and completeness.
Collaborate with cross-functional teams to understand data requirements and optimize data collection processes.
Conduct exploratory data analysis to identify patterns, trends, and anomalies.
Design and implement machine learning models for predictive and prescriptive analytics.
Develop and engineer relevant features to improve model performance and accuracy.
Evaluate model performances
Stay informed about the latest advancements in machine learning and data science techniques.
How this course meets the requirements?
Learn about Data Loading and EDA
Collaborate with team members on various tasks and on Kaggle
Gain knowledge on EDA and Data Insight generation
Understand the concepts of feature engineering, feature selection, baseline model building, model performance analysis and model metrics
Learn about hyperparameter tuning, comparison of models, grid search, cross-validation
Learn and evaluate model performance and compare various models
Get introduced to NLP and advancements in Data Science
Tools you will learn:
Pandas
Numpy functions
Python functions
Kaggle notebooks
Google colab
matplotlib
seaborn
nltk
Scikit learn
XGBoost
LightGBM
Transformers, and many more ML packages and libraries, model metrics: cross entropy, DB Index, etc. NLP, and LLMs for ML tasks (hugging face library)
Extra Module and Benefits:
1. AI Fundamentals and Applications:
Unlock exclusive access to one of our AI modules Learn from our experts leveraging AI to enhance your productivity and understand the wide variety of applications of AI across industries
2. Career Guidance
Understand how to effectively search for a job, find startups, craft a compelling CV and Cover Letter, types of job platforms and many more!
Trainers:
Dr. Chetana Didugu - Germany
Dr. Chetana Didugu is an Experienced Data Scientist, Product Expert, and PhD graduate from IIM Ahmedabad. She has worked 10+ years in various top companies in the world like Amazon, FLIX, Zalando, HCL, etc in topics like Data Analysis and Visualisation, Business Analysis, Product Management, Product Analytics & Data Science. She has trained more than 100 students in this domain till date.
Aravinth Palaniswamy - Germany
Founder of 2 startups in Germany and India, Technology Consultant, and Chief Product Officer of Moyyn, and has 10+ years of experience in Venture Building, Product and Growth Marketing.
Welcome to 'Data Analyst - Job training', a first-of-its kind short program designed for jobseekers and career change aspirants.
This course is specifically created as a JOB-BASED TRAINING program thereby teaching concepts hands-on and relevant to real-work environment. If you are looking for a job in Data Analysis or if you are a student who would like to get first experience in this domain, this course is exactly for you.
How is this program a JOB-BASED TRAINING and how will it equip you with skills relevant for your role:
What companies ask for a Data Analyst role?
Gather and collect data from various sources, ensuring data accuracy and completeness.
Clean and preprocess data to prepare it for analysis.
Clean and preprocess data to prepare it for analysis.
Create reports and dashboards to present key performance indicators (KPIs) and data-driven insights.
Apply statistical methods to analyze data and derive actionable insights.
Conduct hypothesis testing and regression analysis as needed.
Implement and maintain data quality standards.
How this course meets the requirements?
Learn how to import data in Python, usiung kaggle
Learn how to treat input data: distribution, outliers, null and missing values
Gain a deeper understanding in Descriptive Statistics
Master Data visualisation: Graphing etiquettes – which graphs are applicable for what type of data analysis
Gain knowledge on Descriptive Statistics, Inferential Statistics and Predictive Statistics
Understand Inferential statistics: Hypothesis testing, Normal distribution, Central LImit Theorem, Sample vs Population, Sampling, test statistics, Type I and II error
Learn and work with predictive analysis
Tools you will learn:
Pandas
Numpy functions
Statistics
Matplotlib
Seaborn
Python
plotly
dash
Matplotlib
Data Visualisation
Extra Module and Benefits:
1. AI Fundamentals and Applications:
Unlock exclusive access to one of our AI modules Learn from our experts leveraging AI to enhance your productivity and understand the wide variety of applications of AI across industries
2. Career Guidance
Understand how to effectively search for a job, find startups, craft a compelling CV and Cover Letter, types of job platforms and many more!
Trainers:
Dr. Chetana Didugu - Germany
Dr. Chetana Didugu is an Experienced Data Scientist, Product Expert, and PhD graduate from IIM Ahmedabad. She has worked 10+ years in various top companies in the world like Amazon, FLIX, Zalando, HCL, etc in topics like Data Analysis and Visualisation, Business Analysis, Product Management, Product Analytics & Data Science. She has trained more than 100 students in this domain till date.
Aravinth Palaniswamy - Germany
Founder of 2 startups in Germany and India, Technology Consultant, and Chief Product Officer of Moyyn, and has 10+ years of experience in Venture Building, Product and Growth Marketing.
Welcome to 'Data Analyst - Job training', a first-of-its kind short program designed for jobseekers and career change aspirants.
This course is specifically created as a JOB-BASED TRAINING program thereby teaching concepts hands-on and relevant to real-work environment. If you are looking for a job in Data Analysis or if you are a student who would like to get first experience in this domain, this course is exactly for you.
How is this program a JOB-BASED TRAINING and how will it equip you with skills relevant for your role:
What companies ask for a Data Analyst role?
Gather and collect data from various sources, ensuring data accuracy and completeness.
Clean and preprocess data to prepare it for analysis.
Clean and preprocess data to prepare it for analysis.
Create reports and dashboards to present key performance indicators (KPIs) and data-driven insights.
Apply statistical methods to analyze data and derive actionable insights.
Conduct hypothesis testing and regression analysis as needed.
Implement and maintain data quality standards.
How this course meets the requirements?
Learn how to import data in Python, usiung kaggle
Learn how to treat input data: distribution, outliers, null and missing values
Gain a deeper understanding in Descriptive Statistics
Master Data visualisation: Graphing etiquettes – which graphs are applicable for what type of data analysis
Gain knowledge on Descriptive Statistics, Inferential Statistics and Predictive Statistics
Understand Inferential statistics: Hypothesis testing, Normal distribution, Central LImit Theorem, Sample vs Population, Sampling, test statistics, Type I and II error
Learn and work with predictive analysis
Tools you will learn:
Pandas
Numpy functions
Statistics
Matplotlib
Seaborn
Python
plotly
dash
Matplotlib
Data Visualisation
Extra Module and Benefits:
1. AI Fundamentals and Applications:
Unlock exclusive access to one of our AI modules Learn from our experts leveraging AI to enhance your productivity and understand the wide variety of applications of AI across industries
2. Career Guidance
Understand how to effectively search for a job, find startups, craft a compelling CV and Cover Letter, types of job platforms and many more!
Trainers:
Dr. Chetana Didugu - Germany
Dr. Chetana Didugu is an Experienced Data Scientist, Product Expert, and PhD graduate from IIM Ahmedabad. She has worked 10+ years in various top companies in the world like Amazon, FLIX, Zalando, HCL, etc in topics like Data Analysis and Visualisation, Business Analysis, Product Management, Product Analytics & Data Science. She has trained more than 100 students in this domain till date.
Aravinth Palaniswamy - Germany
Founder of 2 startups in Germany and India, Technology Consultant, and Chief Product Officer of Moyyn, and has 10+ years of experience in Venture Building, Product and Growth Marketing.
Artificial Intelligence. The final frontier. For most of us still a book with seven seals. Where should developers start to write their first AI programs? In this course you learn to build Neural Networks and Genetic Algorithms from the ground up. Without frameworks that hide all the interesting stuff in a black box, you are going to build a program that trains self-driving cars. You will learn and assemble all the required building blocks and will be amazed that in no time cars are learning to drive autonomously. There is only one way to learn AI and that is to just pick a project and start building. That is what this course is about!
Target audience
Developers who especially benefit from this course, are:
developers who want to use their basic Python skills to program self-driving cars.
developers who want to understand Neural Networks and Genetic Algorithms by building them from the ground up.
Challenges
Artificial Intelligence is a black box to many developers. The problem is that many AI frameworks hide the details you need to understand how all the individual components work. The solution is to build things from the ground up and learn to create and combine genetic operators and what properties you can change to optimize the result. This course starts with an empty script and shows you every step that is needed to create autonomous cars that learn how to drive on tracks. Once you have seen the building blocks of a Genetic Algorithm, you can use them in your future projects!
What can you do after this course?
define what problems can be solved with Genetic Algorithms
build Neural Networks and Genetic Algorithms from the ground up
take any problem that can be solved with genetic algorithms and solve it by re-using the code you created in this course
Topics
AI Introduction: Neural Networks and the Genetic Algorithm
Car mechanics: Creating a window, drawing backgrounds and cars, controlling the car. Understanding track information
Neural Network: Inputs, outputs, sensors, activation, feed forward
Genetic Algorithm: Fitness, Chromosomes, Selection, Cross over and Mutation
Challenges: Slipping cars, Store the car brain, Stay in the middle of the road and Test Drives
Duration
2 hours video time, 6 hours including typing along.
The teacher
This course is taught by Loek van den Ouweland, a senior software engineer with 25 years of professional experience. Loek is the creator of Wunderlist for windows, Microsoft To-do and Mahjong for Windows and loves to teach software engineering.
Artificial Intelligence. The final frontier. For most of us still a book with seven seals. Where should developers start to write their first AI programs? In this course you learn to build Neural Networks and Genetic Algorithms from the ground up. Without frameworks that hide all the interesting stuff in a black box, you are going to build a program that trains self-driving cars. You will learn and assemble all the required building blocks and will be amazed that in no time cars are learning to drive autonomously. There is only one way to learn AI and that is to just pick a project and start building. That is what this course is about!
Target audience
Developers who especially benefit from this course, are:
developers who want to use their basic Python skills to program self-driving cars.
developers who want to understand Neural Networks and Genetic Algorithms by building them from the ground up.
Challenges
Artificial Intelligence is a black box to many developers. The problem is that many AI frameworks hide the details you need to understand how all the individual components work. The solution is to build things from the ground up and learn to create and combine genetic operators and what properties you can change to optimize the result. This course starts with an empty script and shows you every step that is needed to create autonomous cars that learn how to drive on tracks. Once you have seen the building blocks of a Genetic Algorithm, you can use them in your future projects!
What can you do after this course?
define what problems can be solved with Genetic Algorithms
build Neural Networks and Genetic Algorithms from the ground up
take any problem that can be solved with genetic algorithms and solve it by re-using the code you created in this course
Topics
AI Introduction: Neural Networks and the Genetic Algorithm
Car mechanics: Creating a window, drawing backgrounds and cars, controlling the car. Understanding track information
Neural Network: Inputs, outputs, sensors, activation, feed forward
Genetic Algorithm: Fitness, Chromosomes, Selection, Cross over and Mutation
Challenges: Slipping cars, Store the car brain, Stay in the middle of the road and Test Drives
Duration
2 hours video time, 6 hours including typing along.
The teacher
This course is taught by Loek van den Ouweland, a senior software engineer with 25 years of professional experience. Loek is the creator of Wunderlist for windows, Microsoft To-do and Mahjong for Windows and loves to teach software engineering.
Welcome to 'AI for everyone - Job-based training 2024', a first-of-its kind short program designed for students, working professionals, managers, jobseekers and career change aspirants who are interested in understanding the fundamentals of AI.
"AI for Everyone" is a comprehensive course designed to demystify the field of artificial intelligence (AI) for a broad audience. It introduces the basic concepts of AI, its potential impacts on various industries, and ethical considerations. The course aims to make AI understandable and accessible, regardless of technical background. Participants will learn how AI technologies work, explore real-world AI applications, understand AI's societal implications, and get insights into future trends. This course is ideal for non-technical individuals curious about AI, as well as professionals seeking foundational AI knowledge to apply in their fields.
Extra Module and Benefits:
Career Guidance module:
Understand how to effectively search for a job, find startups, craft a compelling CV and Cover Letter, types of job platforms and many more!
Trainers:
Dr. Chetana Didugu - Germany
Dr. Chetana Didugu is an Experienced Data Scientist, Product Expert, and PhD graduate from IIM Ahmedabad. She has worked 10+ years in various top companies in the world like Amazon, FLIX, Zalando, HCL, etc in topics like Data Analysis and Visualisation, Business Analysis, Product Management, Product Analytics & Data Science. She has trained more than 100 students in this domain till date.
Aravinth Palaniswamy - Germany
Founder of 2 startups in Germany and India, Technology Consultant, and Chief Product Officer of Moyyn, and has 10+ years of experience in Venture Building, Product and Growth Marketing.
Welcome to 'AI for everyone - Job-based training 2024', a first-of-its kind short program designed for students, working professionals, managers, jobseekers and career change aspirants who are interested in understanding the fundamentals of AI.
"AI for Everyone" is a comprehensive course designed to demystify the field of artificial intelligence (AI) for a broad audience. It introduces the basic concepts of AI, its potential impacts on various industries, and ethical considerations. The course aims to make AI understandable and accessible, regardless of technical background. Participants will learn how AI technologies work, explore real-world AI applications, understand AI's societal implications, and get insights into future trends. This course is ideal for non-technical individuals curious about AI, as well as professionals seeking foundational AI knowledge to apply in their fields.
Extra Module and Benefits:
Career Guidance module:
Understand how to effectively search for a job, find startups, craft a compelling CV and Cover Letter, types of job platforms and many more!
Trainers:
Dr. Chetana Didugu - Germany
Dr. Chetana Didugu is an Experienced Data Scientist, Product Expert, and PhD graduate from IIM Ahmedabad. She has worked 10+ years in various top companies in the world like Amazon, FLIX, Zalando, HCL, etc in topics like Data Analysis and Visualisation, Business Analysis, Product Management, Product Analytics & Data Science. She has trained more than 100 students in this domain till date.
Aravinth Palaniswamy - Germany
Founder of 2 startups in Germany and India, Technology Consultant, and Chief Product Officer of Moyyn, and has 10+ years of experience in Venture Building, Product and Growth Marketing.
A/B Testing in R is a course offered by Code Learn Academy , focusing on the exploration of A/B testing using the R programming language. A/B testing is a common experimental design employed in both industry and academia to investigate human behavior. These tests compare two variables to determine if there is a significant difference in performance measurements and whether the measurements vary significantly by a meaningful method. By mastering A/B testing and interpreting results, you can make data-driven decisions and predictions.
In this course, you will learn what questions A/B tests answer, essential considerations for A/B testing, how to respond to existing questions, and how to visualize data. You will also discover how to determine the required sample size for an experiment, perform appropriate analyses for data and existing hypotheses, ensure that results can be confidently considered, and present results to an audience without statistical background. The course covers both parametric and non-parametric A/B tests, such as the t-test, Mann-Whitney U test, Chi-Square independence test, Fisher's exact test, and Pearson and Spearman correlation. Additionally, power analysis will be examined for each test.
The AI Ethics course has been released by the Code Learn Academy. This introductory course on artificial intelligence ethics provides a comprehensive overview of ethical considerations in the rapidly evolving field of artificial intelligence. It encompasses industry, policy-making, academia, and society in general, covering the principles of AI ethics, strategies for fostering fair and just artificial intelligence systems, methods for minimizing biases, and approaches to addressing key issues and building user trust. Throughout this course, you will learn the principles of ethical artificial intelligence and expand your understanding of common challenges and opportunities in the field of AI ethics. Through practical exercises, you will develop the skills to create ethical artificial intelligence.
The Artificial Intelligence (AI) Strategy course has been released by the Code Learn Academy. You've likely heard about various strategies such as business, data, and artificial intelligence, and have been amazed by how they interconnect. To understand how to integrate these intertwined strategies to create a robust strategic framework for organizations active in today's data-centric world, take this course. Additionally, you will explore the role of an AI strategist in successfully transforming artificial intelligence that aligns well with business strategic objectives.
When formulating an effective artificial intelligence strategy, you will begin by understanding the differences between artificial intelligence and traditional software. Such distinctions aid in developing the skill of appropriately discerning the suitability of artificial intelligence. You will also learn to set realistic business goals and define appropriate criteria for project success. As you progress, you'll gather information about evaluating the return on investment for projects that lead to the creation of such complex technology.
Data Fluency, a course on data literacy, is published by the Code Learn Academy. Data is ubiquitous, and in today's data-centric world, being data-fluent is not just a necessity for individuals but also for entire organizations. Data fluency is not only about understanding data but also about the ability to work with and effectively use data for data-driven decision-making.
This course introduces you to the exciting concept of data fluency, covering the best practices and essential skills required to master data fluency. You will start by learning the meaning of data fluency and its distinction from data literacy. Additionally, you will become familiar with the significance of data fluency in today's world.
The course provides a framework for achieving data fluency at both individual and organizational levels. Subsequently, you will explore the data-centric behaviors of individuals along with the skills they use, from identifying business problems with data from the initial stage to conveying information effectively for decision-making.
The "Data Preparation in Excel" course has been released by the Code Learn Academy. In this course, you will become familiar with the process of preparing and cleaning raw data in Excel spreadsheets. The lesson guides you on utilizing the various features available in Excel, enabling you to import data from different sources. Through filtering, sorting, and organizing your columns and rows, you'll learn to prepare your data for subsequent analyses in the most effective manner possible.
In addition to the internal features provided by Excel, you will learn to use various functions for managing and manipulating dates and text strings. Familiarity with logical functions will empower you to create new flags and classifications in your raw data. Furthermore, you'll understand how to combine different logical functions in nested formulas. The course also covers the usage of search functions in Excel to import data from various sheets and identify specific results in large datasets.
Lastly, the course provides an overview of PivotTables, a powerful Excel feature that allows you to summarize and analyze large volumes of data using dynamic tables.
"Data Visualization in Excel" is a course offered by the Code Learn Academy. In this course, you will delve into the fundamentals of Excel charts, equipping yourself with the skills to create impactful visualizations and customize various chart types. With a comprehensive understanding of series and categories, you will gain the expertise needed to transform data into engaging narratives that captivate your audience. Explore working with dual-axis series and create more advanced charts such as bullet charts, waterfall charts, or scatter plots. Additionally, we will examine various chart editing options. Data visualization, like any other field, has its best practices, and it's time to take a closer look at do's and don'ts. We will enhance our skills in selecting chart elements, using colors, legends, and labels, learning how to troubleshoot and customize visual weak points for the benefit of end users.
The course "Deep Learning for Text with PyTorch" is published by Code Learn Academy. Embark on an exciting journey of deep learning for text with PyTorch. This course introduces you to the skills of dealing with various challenges related to text. You will become familiar with the principles of text processing, including encoding and embedding. Various models such as CNNs, RNNs, GANs, and pre-trained models will be applied using textual data. Finally, you will delve into advanced topics such as transfer learning techniques, attention mechanisms, and how to safeguard your models against adversarial attacks.
By the end of this course, you will have the skills to build powerful deep learning models for text. Explore text classification and its role in Natural Language Processing (NLP). Apply your skills to implement word embeddings and develop Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification using PyTorch. Understand how to evaluate your models using appropriate metrics.
The course "Dimensionality Reduction in R" has been released by Code Learn Academy. Have you ever worked with datasets containing numerous features? Do you really need all these features? Which ones are more important? In this course, you will learn dimensionality reduction techniques that help simplify your data and models, allowing you to retain the essential information in your original data and achieve good predictive performance with the models you build. We live in the age of information, where the skill of extracting meaningful insights from data is lucrative. Models trained on reduced data learn faster. In production, smaller models mean quicker response times. Perhaps most importantly, understanding your data and building smaller models is key. Dimensionality reduction is your winning edge in the field of data science. You'll learn the difference between feature selection and feature extraction using R, identifying and removing features with little or redundant information while retaining features with the most information. This is feature selection. You'll also learn how to extract combinations of features as compact components that contain maximum information.
The "End-to-End Machine Learning" course, published by the Code Learn Academy, guides learners through the intricacies of designing, training, and deploying machine learning models. In this comprehensive course, you will delve into the world of machine learning, discovering how to design, train, and deploy final models. Through engaging examples and practical exercises, you will learn to tackle complex data challenges and build powerful ML models. By the end of this course, you will be equipped with the skills needed to create, monitor, and maintain high-performance models, along with practical insights.
You will start by learning the principles of Exploratory Data Analysis (EDA) and data preparation. You'll clean and preprocess your data, ensuring it's ready for model training. Then, you'll master the art of feature engineering and selection to optimize your models for real-world challenges.
The course covers using the Boruta library for feature selection, recording experiments with MLFlow, and fine-tuning models using k-fold cross-validation. You'll uncover the secrets of effective error metrics and explore the importance of feature stores and model registries in the context of end-to-end machine learning frameworks. You'll also learn how to monitor and evaluate your model's performance over time using Docker and AWS. The concept of data drift and how to detect it using statistical tests will be comprehensively understood.
Feedback loops, retraining, and labeling strategies will be implemented to maintain the performance of your models in the face of ever-changing data.
A/B Testing in R is a course offered by Code Learn Academy , focusing on the exploration of A/B testing using the R programming language. A/B testing is a common experimental design employed in both industry and academia to investigate human behavior. These tests compare two variables to determine if there is a significant difference in performance measurements and whether the measurements vary significantly by a meaningful method. By mastering A/B testing and interpreting results, you can make data-driven decisions and predictions.
In this course, you will learn what questions A/B tests answer, essential considerations for A/B testing, how to respond to existing questions, and how to visualize data. You will also discover how to determine the required sample size for an experiment, perform appropriate analyses for data and existing hypotheses, ensure that results can be confidently considered, and present results to an audience without statistical background. The course covers both parametric and non-parametric A/B tests, such as the t-test, Mann-Whitney U test, Chi-Square independence test, Fisher's exact test, and Pearson and Spearman correlation. Additionally, power analysis will be examined for each test.
The AI Ethics course has been released by the Code Learn Academy. This introductory course on artificial intelligence ethics provides a comprehensive overview of ethical considerations in the rapidly evolving field of artificial intelligence. It encompasses industry, policy-making, academia, and society in general, covering the principles of AI ethics, strategies for fostering fair and just artificial intelligence systems, methods for minimizing biases, and approaches to addressing key issues and building user trust. Throughout this course, you will learn the principles of ethical artificial intelligence and expand your understanding of common challenges and opportunities in the field of AI ethics. Through practical exercises, you will develop the skills to create ethical artificial intelligence.
The Artificial Intelligence (AI) Strategy course has been released by the Code Learn Academy. You've likely heard about various strategies such as business, data, and artificial intelligence, and have been amazed by how they interconnect. To understand how to integrate these intertwined strategies to create a robust strategic framework for organizations active in today's data-centric world, take this course. Additionally, you will explore the role of an AI strategist in successfully transforming artificial intelligence that aligns well with business strategic objectives.
When formulating an effective artificial intelligence strategy, you will begin by understanding the differences between artificial intelligence and traditional software. Such distinctions aid in developing the skill of appropriately discerning the suitability of artificial intelligence. You will also learn to set realistic business goals and define appropriate criteria for project success. As you progress, you'll gather information about evaluating the return on investment for projects that lead to the creation of such complex technology.
Data Fluency, a course on data literacy, is published by the Code Learn Academy. Data is ubiquitous, and in today's data-centric world, being data-fluent is not just a necessity for individuals but also for entire organizations. Data fluency is not only about understanding data but also about the ability to work with and effectively use data for data-driven decision-making.
This course introduces you to the exciting concept of data fluency, covering the best practices and essential skills required to master data fluency. You will start by learning the meaning of data fluency and its distinction from data literacy. Additionally, you will become familiar with the significance of data fluency in today's world.
The course provides a framework for achieving data fluency at both individual and organizational levels. Subsequently, you will explore the data-centric behaviors of individuals along with the skills they use, from identifying business problems with data from the initial stage to conveying information effectively for decision-making.
The "Data Preparation in Excel" course has been released by the Code Learn Academy. In this course, you will become familiar with the process of preparing and cleaning raw data in Excel spreadsheets. The lesson guides you on utilizing the various features available in Excel, enabling you to import data from different sources. Through filtering, sorting, and organizing your columns and rows, you'll learn to prepare your data for subsequent analyses in the most effective manner possible.
In addition to the internal features provided by Excel, you will learn to use various functions for managing and manipulating dates and text strings. Familiarity with logical functions will empower you to create new flags and classifications in your raw data. Furthermore, you'll understand how to combine different logical functions in nested formulas. The course also covers the usage of search functions in Excel to import data from various sheets and identify specific results in large datasets.
Lastly, the course provides an overview of PivotTables, a powerful Excel feature that allows you to summarize and analyze large volumes of data using dynamic tables.
"Data Visualization in Excel" is a course offered by the Code Learn Academy. In this course, you will delve into the fundamentals of Excel charts, equipping yourself with the skills to create impactful visualizations and customize various chart types. With a comprehensive understanding of series and categories, you will gain the expertise needed to transform data into engaging narratives that captivate your audience. Explore working with dual-axis series and create more advanced charts such as bullet charts, waterfall charts, or scatter plots. Additionally, we will examine various chart editing options. Data visualization, like any other field, has its best practices, and it's time to take a closer look at do's and don'ts. We will enhance our skills in selecting chart elements, using colors, legends, and labels, learning how to troubleshoot and customize visual weak points for the benefit of end users.
The course "Deep Learning for Text with PyTorch" is published by Code Learn Academy. Embark on an exciting journey of deep learning for text with PyTorch. This course introduces you to the skills of dealing with various challenges related to text. You will become familiar with the principles of text processing, including encoding and embedding. Various models such as CNNs, RNNs, GANs, and pre-trained models will be applied using textual data. Finally, you will delve into advanced topics such as transfer learning techniques, attention mechanisms, and how to safeguard your models against adversarial attacks.
By the end of this course, you will have the skills to build powerful deep learning models for text. Explore text classification and its role in Natural Language Processing (NLP). Apply your skills to implement word embeddings and develop Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification using PyTorch. Understand how to evaluate your models using appropriate metrics.
The course "Dimensionality Reduction in R" has been released by Code Learn Academy. Have you ever worked with datasets containing numerous features? Do you really need all these features? Which ones are more important? In this course, you will learn dimensionality reduction techniques that help simplify your data and models, allowing you to retain the essential information in your original data and achieve good predictive performance with the models you build. We live in the age of information, where the skill of extracting meaningful insights from data is lucrative. Models trained on reduced data learn faster. In production, smaller models mean quicker response times. Perhaps most importantly, understanding your data and building smaller models is key. Dimensionality reduction is your winning edge in the field of data science. You'll learn the difference between feature selection and feature extraction using R, identifying and removing features with little or redundant information while retaining features with the most information. This is feature selection. You'll also learn how to extract combinations of features as compact components that contain maximum information.
The "End-to-End Machine Learning" course, published by the Code Learn Academy, guides learners through the intricacies of designing, training, and deploying machine learning models. In this comprehensive course, you will delve into the world of machine learning, discovering how to design, train, and deploy final models. Through engaging examples and practical exercises, you will learn to tackle complex data challenges and build powerful ML models. By the end of this course, you will be equipped with the skills needed to create, monitor, and maintain high-performance models, along with practical insights.
You will start by learning the principles of Exploratory Data Analysis (EDA) and data preparation. You'll clean and preprocess your data, ensuring it's ready for model training. Then, you'll master the art of feature engineering and selection to optimize your models for real-world challenges.
The course covers using the Boruta library for feature selection, recording experiments with MLFlow, and fine-tuning models using k-fold cross-validation. You'll uncover the secrets of effective error metrics and explore the importance of feature stores and model registries in the context of end-to-end machine learning frameworks. You'll also learn how to monitor and evaluate your model's performance over time using Docker and AWS. The concept of data drift and how to detect it using statistical tests will be comprehensively understood.
Feedback loops, retraining, and labeling strategies will be implemented to maintain the performance of your models in the face of ever-changing data.