Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA etc

What you’ll learn

  • 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, such as 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.
  • The use of machine learning libraries such as scikit-learn and pandas to build and evaluate models.
  • Hands-on experience working on real-world datasets and projects that will give students the opportunity to apply the concepts and techniques learned throughout.
  • The ability to analyze, interpret and present the results of machine learning models.
  • Understanding of the trade-offs between different machine learning algorithms, and their advantages and disadvantages.
  • Understanding of the best practices for developing, implementing, and interpreting machine learning models.
  • Skills in troubleshooting common machine learning problems and debugging machine learning models.

Requirements

  • Some Concept 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. Throughout the course, you will learn the concepts and techniques necessary to build and evaluate machine-learning models using real-world datasets.

We cover 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 common machine learning algorithms, such as linear regression, k-nearest neighbors, and decision trees.

ML Prerequisites Lectures

  1. Python Crash Course: It is an introductory level course that is designed to help learners quickly learn the basics of Python programming language.

  2. Numpy: It is a library in Python that provides support for large multi-dimensional arrays of homogeneous data types, and a large collection of high-level mathematical functions to operate on these arrays.

  3. Pandas: It is a library in Python that provides easy-to-use data structures and data analysis tools. It is built on top of Numpy and is widely used for data cleaning, transformation, and manipulation.

  4. Matplotlib: It is a plotting library in Python that provides a wide range of visualization tools and support for different types of plots. It is widely used for data exploration and visualization.

  5. Seaborn: It is a library built on top of Matplotlib that provides higher-level APIs for easier and more attractive plotting. It is widely used for statistical data visualization.

  6. Plotly: It is an open-source library in Python that provides interactive and web-based visualizations. It supports a wide range of plots and is widely used for creating interactive dashboards and data visualization for the web.

ML Models Covered in This Course

  1. Linear Regression: A supervised learning algorithm used for predicting a continuous target variable based on a set of independent variables. It assumes a linear relationship between the independent and dependent variables.

  2. Logistic Regression: A supervised learning algorithm used for predicting a binary outcome based on a set of independent variables. It uses a logistic function to model the probability of the outcome.

  3. Decision Trees: A supervised learning algorithm that uses a tree-like model of decisions and their possible consequences. It is often used for classification and regression tasks.

  4. Random Forest: A supervised learning algorithm that combines multiple decision trees to increase the accuracy and stability of the predictions. It is an ensemble method that reduces overfitting and improves the generalization of the model.

  5. Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks. It finds the best boundary (or hyperplane) that separates the different classes in the data.

  6. K-Nearest Neighbors (KNN): A supervised learning algorithm used for classification and regression tasks. It finds the k nearest points to a new data point and classifies it based on the majority class of the k nearest points.

  7. Hyperparameter Tuning: It is the process of systematically searching for the best combination of hyperparameters for a machine learning model. It is used to optimize the performance of the model and to prevent overfitting by finding the optimal set of parameters that work well on unseen data.

  8. AdaBoost: A supervised learning algorithm that adapts to the data by adjusting the weights of the observations. It is an ensemble method that is used for classification tasks.

  9. XGBoost: A supervised learning algorithm that is an extension of a gradient boosting algorithm. It is widely used in Kaggle competitions and industry projects.

  10. CatBoost: A supervised learning algorithm that is designed to handle categorical variables effectively.

Unsupervised Models

Clustering algorithms can be broadly classified into three types: centroid-based, density-based, and hierarchical. Centroid-based clustering algorithms such as k-means, group data points based on their proximity to a centroid, or center point. Density-based clustering algorithms such as DBSCAN, group data points based on their density in the feature space. Hierarchical clustering algorithms such as Agglomerative and Divisive build a hierarchy of clusters by either merging or dividing clusters iteratively.

  1. K-Means: A centroid-based clustering algorithm that groups data points based on their proximity to a centroid. It is widely used for clustering large datasets.

  2. DBSCAN: A density-based clustering algorithm that groups data points based on their density in the feature space. It is useful for identifying clusters of arbitrary shape.

  3. Hierarchical Clustering: An algorithm that builds a hierarchy of clusters by merging or dividing clusters iteratively. It can be agglomerative or divisive in nature.

  4. Spectral Clustering: A clustering algorithm that finds clusters by using eigenvectors of the similarity matrix of the data.

  5. Principal Component Analysis (PCA): A dimensionality reduction technique that projects data onto a lower-dimensional space while preserving the most important information.

Advanced Models

  1. 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 sets of input data onto a set of 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 automatically learn representations of the data at different levels of abstraction.

  2. 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. The importance increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus. Tf-idf is used in NLP for tasks such as text classification, text clustering, and information retrieval. It is also used in document summarization and feature extraction for text data.

Are there any course requirements or prerequisites?

  • No introductory skill level of Python programming required

  • Have a computer (either Mac, Windows, or Linux)

  • Desire to learn!

Who this course is for:

  • Beginners python programmers.

  • Beginners Data Science programmers.

  • Students of Data Science and Machine Learning.

  • Anyone interested in learning more about python, data science, or data visualizations.

  • Anyone interested in the rapidly expanding world of data science!

  • Developers who want to work in analytics and visualization projects.

  • Anyone who wants to explore and understand data before applying machine learning.

Throughout the course, you will have access to a team of experienced instructors who will provide guidance and support as you work on your projects. You will also have access to a community of fellow students who will provide additional support and feedback as you work on your projects.

The course is self-paced, which means you can complete the modules and projects at your own pace,

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.
  • Anyone who is curious about machine learning and wants to learn more about its applications and how it is used in the industry.