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 "Artificial Intelligence with Python: Real-World MCQ Test." This course is designed to help you master the fundamental concepts of artificial intelligence (AI) using Python by providing six practice tests featuring real-world scenario-based multiple-choice questions (MCQs). Each practice test is supported by detailed explanations to enhance your understanding of AI concepts. With a 30-minute time duration for each practice test and a passing score requirement of 50%, this course is tailored to prepare you for real-world AI challenges.
Course Overview: In this course, you will have the opportunity to assess and enhance your AI skills using Python through a series of practice tests. These tests are thoughtfully designed to simulate real-world scenarios, enabling you to apply your knowledge effectively.
Practice Tests:
AI Fundamentals: Test your foundational knowledge of AI concepts, including machine learning and deep learning.
Machine Learning: Evaluate your understanding of machine learning algorithms, supervised and unsupervised learning, and model evaluation.
Deep Learning: Challenge yourself with questions related to neural networks, deep learning frameworks, and image recognition.
Natural Language Processing (NLP): Assess your skills in NLP techniques, sentiment analysis, and text classification.
Reinforcement Learning: Test your proficiency in reinforcement learning, Q-learning, and policy optimization.
Real-World AI Project: Demonstrate your skills by working on a comprehensive AI project that encompasses various aspects of AI development.
Time Duration: Each practice test has a time limit of 30 minutes, demanding quick thinking and informed decision-making, just like you would encounter in real-world AI scenarios.
Passing Score: To successfully complete each practice test and advance in this course, you must achieve a passing score of at least 50%. This ensures that you have a strong grasp of the material and are well-prepared for practical AI tasks.
Course Outcome: Upon completing this course, you will:
Have a solid foundation in artificial intelligence using Python.
Be proficient in machine learning, deep learning, and natural language processing.
Understand reinforcement learning and its applications.
Be well-prepared to tackle real-world AI challenges and projects.
Who Is This Course For: This course is ideal for individuals who want to excel in artificial intelligence using Python, including:
Aspiring data scientists, AI engineers, and machine learning practitioners looking to enhance their Python-based AI skills.
Students and professionals aiming to enter the field of artificial intelligence.
Anyone interested in mastering AI concepts and working on real-world AI projects.
Prerequisites: To maximize your success in this course, it is recommended that you have a basic understanding of Python programming. Familiarity with machine learning and AI concepts is beneficial but not mandatory.
Conclusion: "Artificial Intelligence with Python: Real-World MCQ Test" is a practical and hands-on course designed to boost your confidence and proficiency in artificial intelligence using Python. By providing real-world scenario-based practice tests with detailed explanations, our goal is to equip you with the skills and knowledge needed to excel in the field of artificial intelligence. Start your journey to becoming a proficient AI practitioner today!
Welcome to "Artificial Intelligence with Python: Real-World MCQ Test." This course is designed to help you master the fundamental concepts of artificial intelligence (AI) using Python by providing six practice tests featuring real-world scenario-based multiple-choice questions (MCQs). Each practice test is supported by detailed explanations to enhance your understanding of AI concepts. With a 30-minute time duration for each practice test and a passing score requirement of 50%, this course is tailored to prepare you for real-world AI challenges.
Course Overview: In this course, you will have the opportunity to assess and enhance your AI skills using Python through a series of practice tests. These tests are thoughtfully designed to simulate real-world scenarios, enabling you to apply your knowledge effectively.
Practice Tests:
AI Fundamentals: Test your foundational knowledge of AI concepts, including machine learning and deep learning.
Machine Learning: Evaluate your understanding of machine learning algorithms, supervised and unsupervised learning, and model evaluation.
Deep Learning: Challenge yourself with questions related to neural networks, deep learning frameworks, and image recognition.
Natural Language Processing (NLP): Assess your skills in NLP techniques, sentiment analysis, and text classification.
Reinforcement Learning: Test your proficiency in reinforcement learning, Q-learning, and policy optimization.
Real-World AI Project: Demonstrate your skills by working on a comprehensive AI project that encompasses various aspects of AI development.
Time Duration: Each practice test has a time limit of 30 minutes, demanding quick thinking and informed decision-making, just like you would encounter in real-world AI scenarios.
Passing Score: To successfully complete each practice test and advance in this course, you must achieve a passing score of at least 50%. This ensures that you have a strong grasp of the material and are well-prepared for practical AI tasks.
Course Outcome: Upon completing this course, you will:
Have a solid foundation in artificial intelligence using Python.
Be proficient in machine learning, deep learning, and natural language processing.
Understand reinforcement learning and its applications.
Be well-prepared to tackle real-world AI challenges and projects.
Who Is This Course For: This course is ideal for individuals who want to excel in artificial intelligence using Python, including:
Aspiring data scientists, AI engineers, and machine learning practitioners looking to enhance their Python-based AI skills.
Students and professionals aiming to enter the field of artificial intelligence.
Anyone interested in mastering AI concepts and working on real-world AI projects.
Prerequisites: To maximize your success in this course, it is recommended that you have a basic understanding of Python programming. Familiarity with machine learning and AI concepts is beneficial but not mandatory.
Conclusion: "Artificial Intelligence with Python: Real-World MCQ Test" is a practical and hands-on course designed to boost your confidence and proficiency in artificial intelligence using Python. By providing real-world scenario-based practice tests with detailed explanations, our goal is to equip you with the skills and knowledge needed to excel in the field of artificial intelligence. Start your journey to becoming a proficient AI practitioner today!
Welcome to "Advanced Language Model Studio Development," an immersive course designed to take you through the intricacies of working with OpenAI's Language Model Studio (LM Studio). This course provides an in-depth exploration of various aspects, ranging from fundamental setup and troubleshooting to the practical implementation of language and vision models in real-world applications. Throughout the journey, you'll gain hands-on experience with coding, server management, and app development, elevating your skills in harnessing the power of language models.
Course Structure:
Module 1: Introduction to LM Studio Setup and Troubleshooting (Lectures 001 - 005) In this introductory module, you'll get acquainted with the LM Studio environment. Learn how to set up the server, address common troubleshooting issues, and ensure compatibility between different API versions. Gain insights into the importance of using the correct OpenAI framework version and explore techniques to troubleshoot and resolve version-related challenges.
Module 2: Interacting with LM Studio: Text and Chat Models (Lectures 021 - 023) Delve into the practical aspects of working with text and chat models. Understand the nuances of scripting with OpenAI's API, and explore troubleshooting techniques for varying versions. Witness real-time interactions with the LM Studio backend, and grasp the steps involved in changing responses dynamically using Vim. Develop a comprehensive understanding of the differences between OpenAI framework versions.
Module 3: Transitioning from Local to Online: ENR and Server Accessibility (Lectures 0040 - 0041) Unlock the potential of making your LM Studio server accessible online. Explore the use of End-to-End Red (ENR) technology to transition from a local server to an online server. Learn the intricacies of forwarding domain names to local IP addresses and securing your online server. Witness a step-by-step guide to making your language model accessible from any device, opening up possibilities for broader applications.
Module 4: Monitoring and Managing Requests (Lectures 0041 - 0051) Gain proficiency in monitoring and managing requests made to language models using the Endr architecture and LM Studio interface. Learn to analyze request details, understand response times, and interchange between different language models seamlessly. Acquire insights into optimizing the performance of your language model by monitoring its responses in real-time.
Module 5: iPhone App Development Workflow (Lectures 0050 - 0064) Embark on an exciting journey into mobile app development using LM Studio. Understand the workflow of creating an iPhone app interface, handling network responses, and connecting the app to the LM Studio backend. Follow step-by-step instructions for creating an iOS user interface, connecting UI elements to code, and implementing network requests. Witness the integration of AIManager with LM Studio for interactive and dynamic app experiences.
Module 6: Vision LLM and Image Processing (Lectures 0070 - 0074) Explore the realm of Vision Large Language Models (LLM) and image processing. Learn how to identify objects in images using the Vision script with Python. Dive into the intricacies of preparing image data, creating network requests, and handling responses. Witness the magic of integrating Vision LLM with an iOS app to generate descriptions for images, opening up possibilities for image-based applications.
Module 7: Building Specialized Apps - Travel Recommendations and Language Learning (Lectures 0090 - 0091) Cap off your learning journey by applying your newfound skills to build specialized iPhone apps. Follow the creation of a travel recommendation app and a language learning companion using private language models. Understand how to tailor system messages, modify user interfaces, and implement dynamic responses for specific applications. Explore the endless possibilities of integrating language models into diverse app scenarios.
Conclusion: Once you'll complete the "Advanced Language Model Studio Development" course, you will have acquired a comprehensive skill set, ranging from server setup and troubleshooting to practical app development with LM Studio. This course is your gateway to harnessing the full potential of OpenAI's Language Model Studio in diverse real-world applications. Stay curious, keep coding, and continue pushing the boundaries of what's possible with language models!
Welcome to "Advanced Language Model Studio Development," an immersive course designed to take you through the intricacies of working with OpenAI's Language Model Studio (LM Studio). This course provides an in-depth exploration of various aspects, ranging from fundamental setup and troubleshooting to the practical implementation of language and vision models in real-world applications. Throughout the journey, you'll gain hands-on experience with coding, server management, and app development, elevating your skills in harnessing the power of language models.
Course Structure:
Module 1: Introduction to LM Studio Setup and Troubleshooting (Lectures 001 - 005) In this introductory module, you'll get acquainted with the LM Studio environment. Learn how to set up the server, address common troubleshooting issues, and ensure compatibility between different API versions. Gain insights into the importance of using the correct OpenAI framework version and explore techniques to troubleshoot and resolve version-related challenges.
Module 2: Interacting with LM Studio: Text and Chat Models (Lectures 021 - 023) Delve into the practical aspects of working with text and chat models. Understand the nuances of scripting with OpenAI's API, and explore troubleshooting techniques for varying versions. Witness real-time interactions with the LM Studio backend, and grasp the steps involved in changing responses dynamically using Vim. Develop a comprehensive understanding of the differences between OpenAI framework versions.
Module 3: Transitioning from Local to Online: ENR and Server Accessibility (Lectures 0040 - 0041) Unlock the potential of making your LM Studio server accessible online. Explore the use of End-to-End Red (ENR) technology to transition from a local server to an online server. Learn the intricacies of forwarding domain names to local IP addresses and securing your online server. Witness a step-by-step guide to making your language model accessible from any device, opening up possibilities for broader applications.
Module 4: Monitoring and Managing Requests (Lectures 0041 - 0051) Gain proficiency in monitoring and managing requests made to language models using the Endr architecture and LM Studio interface. Learn to analyze request details, understand response times, and interchange between different language models seamlessly. Acquire insights into optimizing the performance of your language model by monitoring its responses in real-time.
Module 5: iPhone App Development Workflow (Lectures 0050 - 0064) Embark on an exciting journey into mobile app development using LM Studio. Understand the workflow of creating an iPhone app interface, handling network responses, and connecting the app to the LM Studio backend. Follow step-by-step instructions for creating an iOS user interface, connecting UI elements to code, and implementing network requests. Witness the integration of AIManager with LM Studio for interactive and dynamic app experiences.
Module 6: Vision LLM and Image Processing (Lectures 0070 - 0074) Explore the realm of Vision Large Language Models (LLM) and image processing. Learn how to identify objects in images using the Vision script with Python. Dive into the intricacies of preparing image data, creating network requests, and handling responses. Witness the magic of integrating Vision LLM with an iOS app to generate descriptions for images, opening up possibilities for image-based applications.
Module 7: Building Specialized Apps - Travel Recommendations and Language Learning (Lectures 0090 - 0091) Cap off your learning journey by applying your newfound skills to build specialized iPhone apps. Follow the creation of a travel recommendation app and a language learning companion using private language models. Understand how to tailor system messages, modify user interfaces, and implement dynamic responses for specific applications. Explore the endless possibilities of integrating language models into diverse app scenarios.
Conclusion: Once you'll complete the "Advanced Language Model Studio Development" course, you will have acquired a comprehensive skill set, ranging from server setup and troubleshooting to practical app development with LM Studio. This course is your gateway to harnessing the full potential of OpenAI's Language Model Studio in diverse real-world applications. Stay curious, keep coding, and continue pushing the boundaries of what's possible with language models!
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.