Machine Learning Engineering Tools for Beginners
Machine Learning Engineering Tools for Beginners, available at $54.99, has an average rating of 5, with 74 lectures, based on 3 reviews, and has 1013 subscribers.
You will learn about An understanding of the fundamental principles of machine learning. The differences between various types of machine learning: Supervised, Unsupervised, and Reinforcement Learning. Real-world applications of machine learning across different industries. Basics of Python programming, including data types, variables, and operators. How to work with Jupyter Notebooks for Python coding and data analysis. The usage of key Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn. Different types of data: structured and unstructured data. Techniques for data preprocessing: cleaning, transformation, and normalization. How to conduct feature extraction and selection. Understanding and applying descriptive statistics in data analysis. Data visualization techniques using Matplotlib and Seaborn. The concepts of correlation and covariance in data. Implementing basic machine learning algorithms like Linear Regression and Logistic Regression Introduction to classification techniques: Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). Unsupervised learning techniques like K-Means and Hierarchical Clustering. The concepts of overfitting, underfitting and understanding the bias-variance trade-off. Evaluation metrics for regression and classification tasks. Techniques for model validation, including cross-validation. An introduction to deep learning and neural networks. The architecture and applications of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). How to use Scikit-Learn for building and training models. Techniques for hyperparameter tuning and model optimization. An introduction to Natural Language Processing (NLP). Text cleaning and preprocessing techniques for NLP. An overview of basic NLP algorithms. Understanding the concept of bias in machine learning models. Learning about the ethical implications of machine learning. Strategies for reducing bias and promoting fairness in machine learning models. Hands-on experience applying machine learning techniques to real-world datasets. Steps for continuing learning and advancing in the field of Machine Learning Engineering. This course is ideal for individuals who are Absolute Beginners: Individuals with little to no experience in machine learning who wish to gain a solid understanding of the fundamentals. or Programmers and Software Developers: Professionals in the software development field who want to expand their skill set into the AI/ML domain. or Students: Undergraduate or graduate students in computer science, data science, statistics, or related fields who wish to gain practical, hands-on experience in machine learning. or Data Analysts and Data Engineers: Professionals working with data who want to enhance their data analysis skills and learn to apply machine learning to their data sets. or Professionals from Other Fields: Professionals from non-technical fields such as marketing, finance, healthcare, etc., who wish to understand machine learning to leverage its benefits in their respective domains. or AI Enthusiasts: Individuals curious about the field of artificial intelligence and want to gain a foundational understanding of machine learning, one of the key components of AI. or The course is intended to be broadly accessible and is designed to provide a comprehensive, beginner-friendly introduction to the exciting world of machine learning. It is particularly useful for Absolute Beginners: Individuals with little to no experience in machine learning who wish to gain a solid understanding of the fundamentals. or Programmers and Software Developers: Professionals in the software development field who want to expand their skill set into the AI/ML domain. or Students: Undergraduate or graduate students in computer science, data science, statistics, or related fields who wish to gain practical, hands-on experience in machine learning. or Data Analysts and Data Engineers: Professionals working with data who want to enhance their data analysis skills and learn to apply machine learning to their data sets. or Professionals from Other Fields: Professionals from non-technical fields such as marketing, finance, healthcare, etc., who wish to understand machine learning to leverage its benefits in their respective domains. or AI Enthusiasts: Individuals curious about the field of artificial intelligence and want to gain a foundational understanding of machine learning, one of the key components of AI. or The course is intended to be broadly accessible and is designed to provide a comprehensive, beginner-friendly introduction to the exciting world of machine learning.
Enroll now: Machine Learning Engineering Tools for Beginners
Summary
Title: Machine Learning Engineering Tools for Beginners
Price: $54.99
Average Rating: 5
Number of Lectures: 74
Number of Published Lectures: 74
Number of Curriculum Items: 74
Number of Published Curriculum Objects: 74
Original Price: $64.99
Quality Status: approved
Status: Live
What You Will Learn
- An understanding of the fundamental principles of machine learning.
- The differences between various types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
- Real-world applications of machine learning across different industries.
- Basics of Python programming, including data types, variables, and operators.
- How to work with Jupyter Notebooks for Python coding and data analysis.
- The usage of key Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.
- Different types of data: structured and unstructured data.
- Techniques for data preprocessing: cleaning, transformation, and normalization.
- How to conduct feature extraction and selection.
- Understanding and applying descriptive statistics in data analysis.
- Data visualization techniques using Matplotlib and Seaborn.
- The concepts of correlation and covariance in data.
- Implementing basic machine learning algorithms like Linear Regression and Logistic Regression
- Introduction to classification techniques: Decision Trees, Random Forests, and K-Nearest Neighbors (KNN).
- Unsupervised learning techniques like K-Means and Hierarchical Clustering.
- The concepts of overfitting, underfitting and understanding the bias-variance trade-off.
- Evaluation metrics for regression and classification tasks.
- Techniques for model validation, including cross-validation.
- An introduction to deep learning and neural networks.
- The architecture and applications of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
- How to use Scikit-Learn for building and training models.
- Techniques for hyperparameter tuning and model optimization.
- An introduction to Natural Language Processing (NLP).
- Text cleaning and preprocessing techniques for NLP.
- An overview of basic NLP algorithms.
- Understanding the concept of bias in machine learning models.
- Learning about the ethical implications of machine learning.
- Strategies for reducing bias and promoting fairness in machine learning models.
- Hands-on experience applying machine learning techniques to real-world datasets.
- Steps for continuing learning and advancing in the field of Machine Learning Engineering.
Who Should Attend
- Absolute Beginners: Individuals with little to no experience in machine learning who wish to gain a solid understanding of the fundamentals.
- Programmers and Software Developers: Professionals in the software development field who want to expand their skill set into the AI/ML domain.
- Students: Undergraduate or graduate students in computer science, data science, statistics, or related fields who wish to gain practical, hands-on experience in machine learning.
- Data Analysts and Data Engineers: Professionals working with data who want to enhance their data analysis skills and learn to apply machine learning to their data sets.
- Professionals from Other Fields: Professionals from non-technical fields such as marketing, finance, healthcare, etc., who wish to understand machine learning to leverage its benefits in their respective domains.
- AI Enthusiasts: Individuals curious about the field of artificial intelligence and want to gain a foundational understanding of machine learning, one of the key components of AI.
- The course is intended to be broadly accessible and is designed to provide a comprehensive, beginner-friendly introduction to the exciting world of machine learning.
Target Audiences
- Absolute Beginners: Individuals with little to no experience in machine learning who wish to gain a solid understanding of the fundamentals.
- Programmers and Software Developers: Professionals in the software development field who want to expand their skill set into the AI/ML domain.
- Students: Undergraduate or graduate students in computer science, data science, statistics, or related fields who wish to gain practical, hands-on experience in machine learning.
- Data Analysts and Data Engineers: Professionals working with data who want to enhance their data analysis skills and learn to apply machine learning to their data sets.
- Professionals from Other Fields: Professionals from non-technical fields such as marketing, finance, healthcare, etc., who wish to understand machine learning to leverage its benefits in their respective domains.
- AI Enthusiasts: Individuals curious about the field of artificial intelligence and want to gain a foundational understanding of machine learning, one of the key components of AI.
- The course is intended to be broadly accessible and is designed to provide a comprehensive, beginner-friendly introduction to the exciting world of machine learning.
Embark on a journey of discovery and innovation with “Machine Learning Engineering for Beginners: Gateway to Artificial Intelligence,” your foundational course to the fascinating world of machine learning. Crafted with beginners in mind, this course provides a comprehensive, yet easy-to-understand introduction to the revolutionary field of machine learning, equipping you with the fundamental skills to excel as a machine learning engineer.
Our voyage begins with an exploration of what machine learning is, the role it plays within the broader landscape of artificial intelligence, and its widespread applications. You will learn about the different types of machine learning, including Supervised, Unsupervised, and Reinforcement Learning, and their respective real-world applications.
To facilitate your transition into this technical field, the course introduces Python, a powerful and versatile programming language widely used in machine learning. Covering the basics of Python programming, you will learn about different data types, variables, and operators. Also, you’ll delve into the practical use of Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn for data preprocessing, model training, visualization, and more.
As we delve deeper, you will learn about the most important machine learning algorithms like Linear Regression, Decision Trees, Random Forests, and K-means Clustering. The course provides a thorough understanding of these algorithms’ workings, their implementation using Python, and tips on choosing the right algorithm for the problem at hand.
The course addresses key concepts of overfitting, underfitting, and the bias-variance trade-off in machine learning models. Furthermore, it presents techniques such as cross-validation and hyperparameter tuning to improve model performance, which will serve as invaluable tools in your machine learning toolkit.
An exciting part of this course is the introduction to deep learning, providing a sneak peek into neural networks’ captivating world. You will also get acquainted with text data handling, paving your way towards more complex topics like Natural Language Processing (NLP).
Recognizing the ethical implications of machine learning, the course emphasizes the creation of fair, unbiased, and transparent models. As machine learning engineers, we bear the responsibility to use this powerful tool ethically, a point this course strongly underlines.
The culmination of this course is a hands-on, real-world project that will provide a concrete application of the skills and knowledge acquired. This project will empower you to tackle real-life data, conduct analyses, and derive actionable insights, thereby marking your transition from a beginner to a confident practitioner.
“Machine Learning Engineering for Beginners: Gateway to Artificial Intelligence” is not merely a course but a launchpad into the exciting universe of artificial intelligence. It is specifically designed for beginners with little or no prior knowledge of machine learning, promising a robust and user-friendly introduction to this dynamic field. Dive in and explore the power of machine learning as you step into the future of technology.
Course Curriculum
Chapter 1: Introduction to Machine Learning
Lecture 1: Introduction
Lecture 2: Overview of Machine Learning
Lecture 3: Types of Machine Learning
Lecture 4: Supervised Machine Learning
Lecture 5: Real-world Applications of Machine Learning
Chapter 2: Jupyter Notebook and Python Setup
Lecture 1: Introduction to Python
Lecture 2: Introduction to Jupyter Notebook
Lecture 3: Installing Jupyter Notebook
Lecture 4: Running the Jupyter Notebook Server
Lecture 5: Common Jupyter Commands
Lecture 6: Jupyter Notebook Components
Lecture 7: The Notebook Dashboard
Lecture 8: Notebook user interface
Lecture 9: Creating a new notebook
Lecture 10: Python Libraries for Machine Learning
Lecture 11: Installing and Importing Python Libraries
Chapter 3: Python Programming Fundamentals
Lecture 1: Python Expressions
Lecture 2: Python Statements
Lecture 3: Python Code Comments
Lecture 4: Python Data Types
Lecture 5: Casting Data Types
Lecture 6: Python Variables
Lecture 7: Python List
Lecture 8: Python Tuple
Lecture 9: Python Dictionaries
Lecture 10: Python Operators
Lecture 11: Python Conditional Statements
Lecture 12: Python Loops
Lecture 13: Python Functions
Chapter 4: Introduction to Data and Data Manipulation
Lecture 1: Types of Data
Lecture 2: Data Preprocessing
Lecture 3: Feature Extraction and Selection
Chapter 5: Understanding and Visualizing Data
Lecture 1: Descriptive Statistics
Lecture 2: Data Visualization with Matplotlib and Seaborn
Lecture 3: Correlation and Covariance
Chapter 6: Introduction to Algorithms
Lecture 1: Basic Machine Learning Algorithms
Lecture 2: Introduction to Classification
Lecture 3: Introduction to Clustering
Chapter 7: Model Evaluation and Validation
Lecture 1: Understanding Overfitting, Underfitting and Bias-Variance trade-off
Lecture 2: Evaluation Metrics for Regression and Classification
Lecture 3: Cross-Validation Techniques
Chapter 8: Introduction to Neural Networks and Deep Learning
Lecture 1: What is Deep Learning?
Lecture 2: Basics of Neural Networks
Lecture 3: Convolutional Neural Networks (CNNs) and Recurrent Neural Network
Lecture 4: Machine Learning Frameworks
Chapter 9: Implementing Machine Learning with Scikit-Learn
Lecture 1: Introduction to Scikit-Learn
Lecture 2: Building Models with Scikit-Learn
Lecture 3: Hyperparameter Tuning and Model Optimization
Lecture 4: Using scikit-learn
Lecture 5: Creating a basic house value estimator
Lecture 6: Loading a Dataset Part 1
Lecture 7: Loading a Dataset Part 2
Lecture 8: Making predictions – Part 1
Lecture 9: Making predictions – Part 2
Chapter 10: Introduction to Natural Language Processing
Lecture 1: Overview of NLP
Lecture 2: Text Cleaning and Preprocessing Techniques
Lecture 3: Basic NLP Algorithms
Chapter 11: Ethics and Bias in Machine Learning
Lecture 1: Understanding Bias in Machine Learning Models
Lecture 2: Ethical Implications of Machine Learning
Lecture 3: Strategies for Reducing Bias and Promoting Fairness
Chapter 12: Example Machine Learning Project
Lecture 1: Final Project: Applying Machine Learning to a Real-World Dataset
Lecture 2: Examples of Real World Datasets
Lecture 3: Walk through step-by-step example of a real-world machine learning project
Lecture 4: Create a report or presentation explaining your methods, your findings
Lecture 5: Next Steps for Advancing in Machine Learning Engineering
Chapter 13: Exploring Pandas DataFrame
Lecture 1: Kaggle Data Sets
Lecture 2: Tabular Data
Lecture 3: Exploring Pandas DataFrame
Lecture 4: Manipulating a Pandas DataFrame
Lecture 5: What is Data Cleaning
Lecture 6: Basic data cleaning process
Lecture 7: What is data visualization
Lecture 8: Visualizing Qualitative Data
Lecture 9: Visualizing Quantitative Data
Instructors
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Bluelime Learning Solutions
Making Learning Simple
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- 4 stars: 0 votes
- 5 stars: 3 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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