Enroll Course: https://www.udemy.com/course/applied-machine-learning-with-python/
Are you looking to dive into the exciting world of Machine Learning? If so, I’ve recently completed ‘Applied Machine Learning With Python’ on Udemy, and I can confidently say it’s an exceptional resource for anyone interested in this powerful field. Designed by seasoned Data Scientists, this course brilliantly breaks down complex theories, algorithms, and coding libraries into easily digestible steps.
The journey begins with essential Data Preprocessing, laying a solid foundation. From there, the course systematically guides you through various regression techniques, including Simple and Multiple Linear Regression, Support Vector Regression (SVR), Decision Trees, and Random Forests. The practical application of these concepts is where this course truly shines, with hands-on exercises using real-life examples.
Next, we delve into Classification, covering a wide array of algorithms like Logistic Regression, K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Naive Bayes, and more. The distinction between different classification methods and their use cases is explained with remarkable clarity.
The course doesn’t stop there. It offers in-depth modules on Clustering (K-Means, Hierarchical Clustering), Association Rule Learning (Apriori, Eclat), and even Reinforcement Learning with Upper Confidence Bound and Thompson Sampling. For those interested in Natural Language Processing (NLP), the course provides a solid introduction to the Bag-of-Words model and relevant algorithms.
What truly sets this course apart is its comprehensive coverage of advanced topics. The Deep Learning section introduces Artificial Neural Networks and Convolutional Neural Networks, updated with TensorFlow 2.0. Dimensionality Reduction techniques like PCA and LDA are also thoroughly explained. Finally, the course concludes with crucial aspects of Model Selection and Boosting, including k-fold Cross-Validation, Parameter Tuning, Grid Search, and top gradient boosting models like XGBoost and CatBoost.
One of the standout features is the inclusion of practical exercises that allow you to build your own models, reinforcing theoretical knowledge with essential hands-on experience. As a bonus, the course provides downloadable Python and R code templates, which are incredibly useful for personal projects. The recent updates in June 2020, ensuring code is up-to-date and incorporating the latest in deep learning and gradient boosting, make this course a future-proof investment.
Whether you’re a beginner looking to understand the fundamentals or an intermediate learner aiming to deepen your expertise, ‘Applied Machine Learning With Python’ offers immense value. It’s an engaging, thorough, and practical guide that I highly recommend for anyone serious about mastering Machine Learning.
Enroll Course: https://www.udemy.com/course/applied-machine-learning-with-python/