Machine learning, artificial intelligence (AI), and data science are among the quickest-growing industries. Indeed, Machine learning and Data science are becoming hot career paths. Companies all around the world are exploring new possibilities and opportunities for deep learning and AI. This growth is continuing to open doors to more jobs in these fields. The Machine Learning, Data Science, and Deep Learning with Python course is here to help you get your feet wet in this exciting field.
Course Details
The course is designed to provide learners with a comprehensive understanding of machine learning, deep learning, and data science from basic to expert level. This course is an excellent source for aspirants who want to learn end to end data science processes along with machine learning techniques.
The course has:
- A high aggregate rating of 4.57663 out of 5.
- Over 28,955 reviews from satisfied learners.
- More than 100 lectures.
- Over 15 hours of video content.
Course Description
Aiden Guillemette, a professional with nine years of experience in Amazon and IMDb, delivers this machine learning tutorial. This course includes long hours of practice sessions, hands-on Python code examples, and additional content on Variational Auto-Encoders and Generative Adversarial Models. Everything is created to help learners in the tech industry to understand and apply the fundamental machine learning, AI, and data mining techniques that real employers demand.
With straightforward explanations, the course provides a clear understanding of the core goals of machine learning, AI, and data science such as model training, data visualization, tuning, and case studies. The lectures cover a wide range of topics, including data preprocessing, unsupervised and supervised learning, deep learning, neural networks, natural language processing, and much more.
The Curriculum
Deep Learning / Neural Networks (MLP’s, CNN’s, and RNN’S) with TensorFlow and Keras
The course provides hands-on experience with how to use TensorFlow and Keras for Deep Neural Networks: MLP's, CNN's, and RNN's. The detailed course design helps learners develop the ability to build their own deep learning models through TensorFlow and Keras.
Data Visualization in Python with MatPlotLib and Seaborn
Data Visualization is a core function of a data scientist. It helps to demonstrate the hidden patterns that data holds. This course covers the two most powerful data visualization libraries used in Python: Matplotlib and Seaborn. Matplotlib is a plotting library that works with Python to provide high-quality 2D and 3D plotting while Seaborn is a data visualization library designed to work with Pandas DataFrame objects. Both of these libraries are essential to explore, study, and validate the dataset for model creation and data analysis.
Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's)
The updated version of the course has extra content on the more advanced method of deep learning: Generative Models. The course covers the creation of synthetic images using two of the most powerful Generative models, Variational Auto-Encoders (VAE’s) and Generative Adversarial Networks (GAN’s). These techniques create new images based on the machine learning model without explicit reproducibility, which is often used in AI Art and image classification.
Is This Course Suitable For You?
If you have programming experience, this course is suitable for you. The course curriculum is designed to cater to both who are experienced in the field of machine learning techniques or those who are newcomers seeking opportunities in the industry.
The course design is flexible and easy to follow for everyone. The course starts with a crash course in Python to give a base understanding of programming to individuals who are new to the language. Every topic is explained by avoiding confusing mathematical notation to help you understand the practical applications of every step.
Additional Features
The course offers an entire section on machine learning with Apache Spark, letting learners scale up the process to analyze big data on a computing network. The course also has a final project that helps learners apply what they've learned in the course.
Ultimately, this course has become a staple for individuals looking to break into the industry.
Verdict
This machine learning tutorial is an excellent way to start your journey into this rapidly growing field. A beginner with a little to no knowledge of data science can quickly get a grasp and become an expert. The course offers value for money, and on completion, learners will gain the required knowledge necessary to become a data scientist. Anyone looking for a data scientist career should enroll right away!
