2023 Python for Machine Learning: A Step-by-Step Guide
Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA, etc.
2023 Python for Machine Learning: A Step-by-Step Guide – Courses Bag
Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA, etc.
What you’ll learn?
2023 Python for Machine Learning: A Step-by-Step Guide – Courses Bag
- The fundamental concepts and techniques of machine learning, including supervised and unsupervised learning
- The implementation of various machine learning algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, etc.
- Techniques for building and evaluating machine learning models include feature selection, feature engineering, and model evaluation techniques.
- The different types of model evaluation metrics, such as accuracy, precision, and recall, and how to interpret them.
- Using machine learning libraries such as sci-kit-learn and pandas to build and evaluate models.
- Hands-on experience working on real-world datasets and projects that will allow students to apply the concepts and techniques learned throughout.
- The ability to analyze, interpret and present the results of machine learning models.
- Understanding the trade-offs between different machine learning algorithms and their advantages and disadvantages.
- Understanding best practices for developing, implementing, and interpreting machine learning models.
- Skills in troubleshooting common machine learning problems and debugging machine learning models.
Requirements
- Some Concepts of Programming
- Elementary Mathematics
- Desire to learn
Description
Welcome to our Machine Learning Projects course! This course is designed for individuals who want to gain hands-on experience in developing and implementing machine learning models.
Also, you will learn the concepts and techniques necessary to build and evaluate machine-learning models using real-world datasets throughout the course.
See Also: Machine Learning & Deep Learning Projects for Beginners 2023
We cover the basics of machine learning, including supervised and unsupervised learning, and the types of problems that can be solved using these techniques. You will also learn about machine learning algorithms like linear regression, k-nearest neighbors, and decision trees.
See Also: The Python for Absolute Beginners Bootcamp
Advanced Models
- Deep Learning Introduction: Deep learning is a subfield of machine learning that uses artificial neural networks with many layers, called deep neural networks, to model and solve complex problems such as image recognition and natural language processing. It is based on the idea that a neural network can learn to automatically learn representations of the data at different levels of abstraction. Multi-layer Perceptron (MLP) is a type of deep learning model that is a feedforward artificial neural network model that maps input data sets onto appropriate outputs. MLP is a supervised learning algorithm that can be used for both classification and regression tasks. MLP is based on the idea that a neural network with multiple layers can learn to learn data representations at different levels of abstraction automatically.
- Natural Language Processing (NLP): Natural Language Processing (NLP) is a field of Artificial Intelligence that deals with the interaction between human language and computers. One of the common techniques used in NLP is the term frequency-inverse document frequency (tf-idf). Tf-idf is a statistical measure that reflects the importance of a word in a document or a corpus of documents.
See Also: The Python for Absolute Beginners Bootcamp
Who this course is for?
- Data scientists, analysts, and engineers who want to expand their knowledge and skills in machine learning.
- Developers and programmers who want to learn how to build and deploy machine learning models in a production environment.
- Researchers and academics who want to understand the latest developments and applications of machine learning.
- Business professionals and managers who want to learn how to apply machine learning to solve real-world problems in their organizations.
- Students and recent graduates who want to gain a solid foundation in machine learning and pursue a career in data science or artificial intelligence.
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