In recent years, demand for artificial intelligence and machine learning has increased, making machine learning expertise increasingly vital for job seekers. Additionally, Python has become the primary language for various machine learning tasks. This article outlines the best machine learning courses in Python and offers readers the opportunity to enhance their skill set, make career transitions, and meet recruiters' expectations.
Machine learning with Python
This course covers the fundamentals of machine learning algorithms and when to use each of them. Teaches how to write Python code to implement techniques like K-Nearest Neighbors (KNN), decision trees, regression trees, etc., and evaluate them.
Specialization in machine learning
The “Machine Learning Specialization” teaches the basics of machine learning and how to create real-world ai applications using it. The course covers numerous supervised and unsupervised learning algorithms and also teaches how to build neural networks using TensorFlow.
Applied machine learning in Python
This course offers hands-on training in applied machine learning, emphasizing techniques over statistical theory. It covers topics such as clustering, predictive modeling, and advanced methods such as ensemble learning using the scikit-learn toolkit.
IBM Machine Learning Professional Certificate
This IBM program offers comprehensive machine learning and deep learning training, covering key algorithms and practices such as ensemble learning, survival analysis, K-means clustering, DBSCAN, dimensionality reduction, etc. Participants also gain hands-on experience with open source frameworks. and libraries such as TensorFlow and Scikit-learn.
Machine Learning Scientist with Python
“Machine Learning Scientist with Python” helps increase the Python skills needed to perform deep, supervised and unsupervised learning. It covers topics such as image processing, cluster analysis, gradient boosting, and popular libraries such as scikit-learn, Spark, and Keras.
Introduction to machine learning
“Introduction to Machine Learning” covers concepts such as logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc., and demonstrates their application in various real-world applications. The course also teaches how to implement these models using Python libraries such as PyTorch.
Machine Learning with Python: From Linear Models to Deep Learning
This course teaches the fundamentals of machine learning and covers classification, regression, clustering, and reinforcement learning. Students learn to implement and analyze models such as linear models, kernel machines, neural networks, and graphical models. They also gain skills in selecting appropriate models for different tasks and effectively managing machine learning projects.
<h3 class="wp-block-heading" id="h-machine-learning-and-ai-with-python”>Machine Learning and ai with Python
This course delved into advanced data science concepts using sample data sets, decision trees, random forests, and various machine learning models. Teaches students how to train models for predictive analytics, interpret results, identify biases in data, and prevent under- or over-fitting.
Specialization in deep learning
This course provides students with the knowledge and skills to understand, develop, and apply deep neural networks in various fields. Through hands-on projects and industry insights, participants master architectures such as CNN, RNN, LSTM, and Transformers using Python and TensorFlow and learn how to tackle real-world ai tasks such as speech recognition, natural language processing, and image recognition.
Introduction to machine learning with TensorFlow
This course introduces machine learning concepts and demonstrates how to use different algorithms to solve real-world problems. He then goes on to explain how neural networks work and how to use the TensorFlow library to create our own image classifier.
Introduction to machine learning with Pytorch
This course is similar to the previous one: “Introduction to machine learning with TensorFlow”. Instead of the TensorFlow library, it covers another Python library widely used in deep learning: Pytorch.
Data Science Fundamentals: K-Means Clustering in Python
This course provides a fundamental understanding of data science, emphasizing the essential mathematics, statistics, and programming skills crucial to data analysis. Through hands-on exercises and a data clustering project, participants gain proficiency in basic concepts, preparing them for more advanced data science courses and real-world applications in various sectors such as finance, retail, and medicine.
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If you would like to suggest any courses we missed from this list, please email us at [email protected]
Shobha is a data analyst with a proven track record in developing innovative machine learning solutions that drive business value.
