Are you interested in learning Machine Learning and how it can be applied to solve real-world problems? Look no further than the 3-in-1 course, "Machine Learning with Python, scikit-learn and TensorFlow". With a rating aggregate of 2.46715 out of 5 and 22 reviews, this comprehensive course covers all the essential aspects of Machine Learning, providing you with everything you need to master algorithms and their implementation.
Course Description
Machine Learning is a blend of computer science and statistics used to build intelligent models capable of solving data-driven problems. The "Machine Learning with Python, scikit-learn and TensorFlow" course focuses heavily on practical approaches to modeling. By the end of this course, you will be able to tackle data-driven problems, implement your solutions, and build efficient models with powerful features of Python, scikit-learn and TensorFlow.
This 3-in-1 course is comprehensive and designed to give you a thorough understanding of all aspects of Machine Learning. The first course, "Step-by-Step Machine Learning with Python", covers all the important fundamentals such as exploratory data analysis, data preprocessing, feature extraction, data visualization, clustering, classification, regression, and model performance evaluation. Moreover, the course is designed to be easy-to-follow with various examples that will walk you through all the essentials you need to build your own models from scratch.
The second course, "Machine Learning with Scikit-learn", covers effective learning algorithms that can be applied to real-world problems using scikit-learn. You'll learn to use scikit-learn's API to extract features from categorical variables, text, and images to evaluate model performance and develop an intuition for how to improve it. Applying the knowledge you'll gain from the second course, you'll be able to build systems that classify documents, recognize images, detect ads, and more.
"Machine Learning with TensorFlow", the third course in the program, covers hands-on examples with Machine Learning using Python. This course provides an opportunity to cover the unique features of the library such as data flow graphs, training, and performance visualization with TensorBoard. These features are covered within an example-rich context using problems from multiple sources. The purpose of this course is to introduce new concepts through problems that are coded and solved over the course of each section, allowing you to build a strong foundation to tackle data-driven problems.
Course Content
The "Machine Learning with Python, scikit-learn and TensorFlow" course includes three complete and comprehensive courses, which are carefully chosen to give you the most effective training possible. Let's take a closer look at what's included in each course.
The first course, "Step-by-Step Machine Learning with Python", covers:
- Exploratory data analysis
- Data preprocessing
- Feature extraction
- Data visualization and clustering
- Classification
- Regression
- Model performance evaluation
- Building models from scratch
The second course, "Machine Learning with Scikit-learn", covers:
- Effective learning algorithms using scikit-learn
- Building systems to classify documents, recognize images, detect ads, and more
- Using scikit-learn's API to extract features from categorical variables, text, and images
- Evaluating model performance
The third course, "Machine Learning with TensorFlow", covers:
- Hands-on examples of machine learning using Python and Tensorflow
- Unique features of the library data flow graphs, training, and visualization of performance with TensorBoard
- Problem-solving with examples from multiple sources
Course Instructors
The course is authored by two experts in the field of Machine Learning.
- Yuxi (Hayden) Liu is an applied research scientist engaged in developing machine learning models and systems for specific learning tasks. He has years of experience as a data scientist and has applied his machine learning expertise in computational advertising.
- Shams Ul Azeem is an undergraduate in Electrical Engineering from NUST Islamabad, Pakistan. He is passionate about computer science and began his journey with Android development. Now, he's pursuing his career in Machine Learning, particularly in deep learning, through medical-related freelancing projects with various companies.
Verdict
If you're looking to learn Machine Learning with practical, hands-on applications, the "Machine Learning with Python, scikit-learn and TensorFlow" course is the perfect solution for you.
This 3-in-1 course covers all aspects of Machine Learning, from fundamentals and exploratory data analysis to building efficient models and evaluating performance. The course is created to be easy-to-follow, with multiple examples that will assist you in building your models from scratch. Moreover, the course contains three separate training programs that have been chosen specifically to provide you clear and comprehensive training. With 22 reviews and a rating aggregate of 2.46715, it is an excellent course for anyone who is interested in Machine Learning.
