Python for Machine Learning in 2023: Step-by-Step Guide

COURSE AUTHOR –
Laxmi Kant | KGP Talkie

Last Updated on November 6, 2023 by GeeksGod

Course : 2023 Python for Machine Learning: A Step-by-Step Guide

Free Udemy Coupon, Python Machine Learning 2023

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

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

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.

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.

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.

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.

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

Linear Regression: A supervised learning algorithm used for predicting a continuous target variable based on a set of independent variables.

Logistic Regression: A supervised learning algorithm used for predicting a binary outcome based on a set of independent variables.

Decision Trees: A supervised learning algorithm that uses a tree-like model of decisions and their possible consequences.

Random Forest: A supervised learning algorithm that combines multiple decision trees to increase the accuracy and stability of the predictions.

Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.

K-Nearest Neighbors (KNN): A supervised learning algorithm used for classification and regression tasks.

Hyperparameter Tuning: It is the process of systematically searching for the best combination of hyperparameters for a machine learning model.

AdaBoost: A supervised learning algorithm that adapts to the data by adjusting the weights of the observations.

XGBoost: A supervised learning algorithm that is an extension of a gradient boosting algorithm.

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.

K-Means: A centroid-based clustering algorithm that groups data points based on their proximity to a centroid.

DBSCAN: A density-based clustering algorithm that groups data points based on their density in the feature space.

Hierarchical Clustering: An algorithm that builds a hierarchy of clusters by merging or dividing clusters iteratively.

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

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

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.

Multi-layer Perceptron (MLP): 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.

Natural Language Processing (NLP): A field of Artificial Intelligence that deals with the interaction between human language and computers.

Term frequency-inverse document frequency (tf-idf): A statistical measure that reflects the importance of a word in a document or a corpus of documents.

Course Requirements

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,

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What you will learn :

1. The fundamental concepts and techniques of machine learning, including supervised and unsupervised learning
2. The implementation of various machine learning algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, etc.
3. Techniques for building and evaluating machine learning models, such as feature selection, feature engineering, and model evaluation techniques.
4. The different types of model evaluation metrics, such as accuracy, precision, and recall and how to interpret them.
5. The use of machine learning libraries such as scikit-learn and pandas to build and evaluate models.
6. Hands-on experience working on real-world datasets and projects that will give students the opportunity to apply the concepts and techniques learned throughout.
7. The ability to analyze, interpret and present the results of machine learning models.
8. Understanding of the trade-offs between different machine learning algorithms, and their advantages and disadvantages.
9. Understanding of the best practices for developing, implementing, and interpreting machine learning models.
10. Skills in troubleshooting common machine learning problems and debugging machine learning models.