Machine Learning: Build Your First AI Model with Python

Name: Machine Learning: Build Your First AI Model with Python
Rating: 4.3 (56 reviews)

Machine Learning with Python: Build Your AI Models and Visualize Insights with Scikit-learn and Matplotlib

Created byAbdurrahman TEKIN

Last updated 1/2024

English

English [Auto],

What you'll learn

Introduction to Machine Learning: Understand the basics of machine learning, its applications, and its importance in today's world.
Python Fundamentals: Learn the essential Python programming concepts and syntax required for machine learning, including variables, data types, loops, condition
Data Preprocessing: Explore the crucial step of data preprocessing, including handling missing data, handling categorical variables, feature scaling, and data n
Learn how to implement them using Python's scikit-learn library.

Course content

2 sections • 14 lectures • 55m total length

Installing Jupyter2:16
Install Jupyter on your computer using pip and launch the notebook in a chosen folder. Create a new Python 3 notebook to write and run code, including hello world.
How to download Python files2:21
Download the Python files from the resources section and save them. Open class one in the Udemy folder with Jupyter notebook to view the code and explanations.

Import datasets2:43
Explore scikit learn's datasets module and use preloaded data sets such as iris and digits to train your first ai model, with step-by-step explanations.
Import train_test_split function1:38
Import the train_test_split function from scikit-learn's model_selection, split data into training and testing sets, train on the training data, and evaluate the percent of correct answers on unseen test data.
Import KNeighborsClassifier2:18
Import the k nearest neighbors classifier from scikit-learn, train with x and y using a training set, predict on test data, and measure mean accuracy with the score function.
Import accuracy_score function2:25
Import the accuracy_score function from scikit-learn metrics to evaluate a model. Compare y_true and y_pred to compute and print the accuracy, as shown by a six-element example with 0.333.
Import matplotlib.pyplot2:29
Import the matplotlib.pyplot module as plt to create and display a simple scatter plot of x and y data with a title, axis labels, and plt.show.
Get iris data5:01
Split data8:13
Split data into x_train, x_test, y_train, and y_test with train_test_split (80/20), train on x_train, test on x_test, and note random_state effects on reproducibility.
create an instance of the `KNeighborsClassifier` class5:30
Create an instance of the KNeighborsClassifier with a chosen number of neighbors, such as three, and examine how nearest neighbors and distance weighting shape predictions to optimize accuracy.
training a k-nearest neighbors classifier1:15
Train a k-nearest neighbors classifier using the fit method with xtrain and ytrain from the training split to build your first AI model in Python.
make predictions on the test set3:03
Train a classifier, predict on X test with the model's predict method, and compare results to Y test to assess accuracy.
By comparing the predicted labels to the true labels and calculating the accurac5:43
Learn to compute model accuracy with the accuracy score by comparing y_test to y_pred, and evaluate performance using an 80% train and 20% test split with a KNN classifier.
Create a scatter plot10:29
Create a scatter plot with two features, visualizing three classes by color map set one, and compare real versus predicted outputs on a train-test split to assess accuracy.

Requirements

Basic Programming Knowledge: Having a basic understanding of programming concepts and syntax will be beneficial. Familiarity with Python programming language is preferred, although the course will cover Python fundamentals as well.
Ensure that you have Python installed on your computer.
Approach the course with a willingness to learn and experiment.

Description

Embark on an exciting journey into the world of machine learning and artificial intelligence with our comprehensive course, "Machine Learning: Build Your First AI Model with Python." Gain the skills and knowledge needed to develop your first AI model using the powerful Python programming language and the popular scikit-learn library.

In this hands-on course, we'll guide you step-by-step through the entire machine learning process, ensuring you grasp each concept and technique thoroughly. Whether you're a beginner or have some programming experience, this course is designed to be accessible and engaging for all skill levels.

The course begins with a solid foundation in Python programming, ensuring you have a strong understanding of the language's syntax and key concepts. You'll learn about variables, data types, conditional statements, loops, functions, and more. Our instructors will provide clear explanations and practical examples to help you master Python fundamentals.

Next, we delve into the essentials of machine learning. We'll explore the key concepts, algorithms, and evaluation metrics, empowering you to understand the inner workings of various machine learning models. Through hands-on exercises and real-world examples, you'll gain a deep understanding of supervised and unsupervised learning, linear regression, logistic regression, decision trees, k-nearest neighbors, and more.

With a solid understanding of machine learning, we'll then dive into the exciting world of deep learning. You'll explore neural networks, activation functions, optimization techniques, and the foundations of popular deep learning libraries such as TensorFlow and PyTorch. Through practical projects and coding exercises, you'll build your first deep learning models and witness their powerful capabilities.

Throughout the course, we emphasize practicality and real-world application. You'll work on engaging projects, such as image classification, sentiment analysis, and recommendation systems, enabling you to apply your newly acquired skills to solve real-world problems. We provide guidance and support as you tackle these projects, ensuring you gain hands-on experience and build a strong portfolio of AI models.

To enhance your learning experience, we've integrated captivating visualizations using the matplotlib library. You'll witness the power of data visualization as you create stunning plots and gain insights into your data and model predictions. These visualizations will deepen your understanding and make your projects visually appealing.

Our instructors are experienced machine learning practitioners who are passionate about teaching. They'll be with you every step of the way, explaining complex concepts in a clear and approachable manner. They'll share their valuable insights, best practices, and practical tips to help you succeed in your AI journey.

By the end of the course, you'll have the skills and confidence to build your first AI model from scratch using Python. You'll be equipped with a strong foundation in machine learning and deep learning, enabling you to explore more advanced topics and contribute to the exciting field of artificial intelligence.

Join us now and unlock the potential of machine learning. Enroll in "Machine Learning: Build Your First AI Model with Python" and embark on an empowering learning experience that will transform your understanding of AI and its practical applications.

Don't miss this opportunity to dive into the fascinating world of machine learning. Enroll today and take the first step towards becoming an AI practitioner!

Who this course is for:

Beginners: If you have little to no prior experience with machine learning or programming, this course provides a solid introduction to the field. The course explains the concepts and techniques in a beginner-friendly manner, starting from the basics and gradually building up your knowledge and skills.

Machine Learning: Build Your First AI Model with Python

What you'll learn

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Course content

Introduction2 lectures • 5min

Course Contents12 lectures • 51min

Requirements

Description

Who this course is for: