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via Udemy |
Go to Course: https://www.udemy.com/course/machine-learning-course-with-python/
The Complete Machine Learning Course in Python is a comprehensive and highly updated program designed to equip learners with the essential skills and knowledge to excel in the rapidly evolving field of machine learning. As of November 2019, this course has been fully refreshed with new sections and improved content to reflect the latest practices and technologies, making it a valuable resource for aspiring data scientists, machine learning enthusiasts, and professionals seeking to enhance their skill set. Course Highlights: - **Up-to-Date Curriculum**: The course covers a broad spectrum of topics, including foundations of deep learning, neural networks, tensor operations, overfitting, regularization, dropout, and validation techniques. It also dives into advanced areas like computer vision with Convolutional Neural Networks, transfer learning, and feature extraction. - **Practical Approach**: Taught by Anthony NG, a senior lecturer in Singapore, the course emphasizes hands-on learning with over 18 hours of content and numerous projects. Students will build a portfolio of 12 machine learning projects to demonstrate their skills and enhance job prospects. - **Tools and Technologies**: The course ensures compatibility with Python 3.6 and 3.7 and is optimized for Google Colab. Learners will develop skills in Jupyter notebooks, Spyder, and other popular IDEs, making it practical and accessible. - **Wide Range of Applications**: Students will learn to solve real-world problems, including classifying flowers, predicting house prices, handwriting recognition, staff attrition prediction, and cancer cell detection. - **Comprehensive Content**: The curriculum covers regression, classification, unsupervised learning, performance metrics, model ensembles, feature engineering, cross-validation, and more. It also introduces advanced algorithms such as SVMs, decision trees, and association rules. - **No Prior Experience Required**: Beginners with basic Python knowledge will find this course suitable, as all code snippets are explained line-by-line, and support is available via Q&A. Review: This course stands out due to its thorough coverage, frequent updates, and project-based teaching approach. The instructor’s step-by-step methodology makes complex concepts accessible, even for newcomers. The emphasis on practical projects ensures that students can immediately apply their knowledge to real-world data. With over fifty 5-star ratings, it clearly resonates with learners worldwide. Recommendation: If you are looking to enter the field of machine learning or want a solid foundation to build your career, this course is highly recommended. It provides the tools, techniques, and project experience necessary to develop competitive machine learning models. Additionally, given the high average salary for machine learning engineers in the U.S., investing in this course could be a financially rewarding decision. In conclusion, The Complete Machine Learning Course in Python by Anthony NG is an excellent choice for anyone serious about mastering machine learning with Python. Its comprehensive, updated content and practical focus make it a standout option for learners of all levels aiming to make a significant impact in data science and artificial intelligence. --- Let me know if you'd like a personalized recommendation based on your background or specific goals!
The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019!With brand new sections as well as updated and improved content, you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practices available to them:Brand new sections include:Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more.Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extractions.And the following sections have all been improved and added to:All the codes have been updated to work with Python 3.6 and 3.7The codes have been refactored to work with Google ColabDeep Learning and NLPBinary and multi-class classifications with deep learningGet the most up to date machine learning information possible, and get it in a single course! * * *The average salary of a Machine Learning Engineer in the US is $166,000! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms.Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival's "project based" teaching style to bring you this hands-on course.With over 18 hours of content and more than fifty 5 star ratings, it's already the longest and best rated Machine Learning course on Udemy!Build Powerful Machine Learning Models to Solve Any ProblemYou'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more! Inside the course, you'll learn how to:Gain complete machine learning tool sets to tackle most real world problemsUnderstand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.Combine multiple models with by bagging, boosting or stackingMake use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your dataDevelop in Jupyter (IPython) notebook, Spyder and various IDECommunicate visually and effectively with Matplotlib and SeabornEngineer new features to improve algorithm predictionsMake use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen dataUse SVM for handwriting recognition, and classification problems in generalUse decision trees to predict staff attritionApply the association rule to retail shopping datasetsAnd much much more!No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q & A area. Make This Investment in YourselfIf you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!Take this course and become a machine learning engineer!