In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Additionally, Python has emerged as the primary language for various ML tasks. This article outlines the top ML courses in Python, offering readers the opportunity to enhance their skill set, transition careers, and meet the expectations of recruiters.
Machine Learning with Python
This course covers the fundamentals of machine learning algorithms and when to use each of them. It teaches writing Python code for implementing techniques like K-Nearest neighbors (KNN), decision trees, regression trees, etc., and evaluating the same.
Machine Learning Specialization
“Machine Learning Specialization” teaches the core concepts of machine learning and how to build real-world AI applications using the same. The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow.
Applied Machine Learning in Python
This course offers practical training in applied machine learning, emphasizing techniques over statistical theory. It covers topics such as clustering, predictive modeling, and advanced methods like ensemble learning using the scikit-learn toolkit.
IBM Machine Learning Professional Certificate
This program by IBM offers comprehensive training in Machine Learning and Deep Learning, covering key algorithms and practices like ensemble learning, survival analysis, K-means clustering, DBSCAN, dimensionality reduction, etc. Participants also gain hands-on experience with open-source frameworks and libraries like TensorFlow and Scikit-learn.
Machine Learning Scientist with Python
“Machine Learning Scientist with Python” helps augment one’s Python skills required for performing supervised, unsupervised, and deep learning. It covers topics like image processing, cluster analysis, gradient boosting, and popular libraries like scikit-learn, Spark, and Keras.
Introduction to Machine Learning
“Introduction to Machine Learning” covers concepts like 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 like PyTorch.
Machine Learning with Python: From Linear Models to Deep Learning
This course teaches the fundamentals of machine learning, covering classification, regression, clustering, and reinforcement learning. Students learn to implement and analyze models like 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.
Machine Learning and AI with Python
This course delved into advanced data science concepts using sample datasets, decision trees, random forests, and various machine learning models. It teaches students to train models for predictive analysis, interpret results, identify data biases, and prevent underfitting or overfitting.
Deep Learning Specialization
This course equips learners with the knowledge and skills to understand, develop, and apply deep neural networks in various fields. Through practical projects and industry insights, participants master architectures like CNNs, RNNs, LSTMs, and Transformers using Python and TensorFlow and learn 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. It then moves on to explain the workings of neural networks and how to use the TensorFlow library to build 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.
Foundations of Data Science: K-Means Clustering in Python
This course provides a foundational understanding of Data Science, emphasizing essential mathematics, statistics, and programming skills crucial for data analysis. Through practical exercises and a data clustering project, participants gain proficiency in core concepts, preparing them for more advanced Data Science courses and real-world applications across various sectors like finance, retail, and medicine.
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Shobha is a data analyst with a proven track record of developing innovative machine-learning solutions that drive business value.
