Data science with Python programming language:

Content:

The initial focus of this course is on the principles of data science, machine learning, and deep learning. As time goes on, the course's lectures and content develop and become more practical. However, the introduction of Python is covered first. If we explicitly examine from the standpoint of Data Science, Machine learning, and deep learning, there is no other choice but "python" as a programming language. Python is one of the fastest-growing programming languages. First, for those who are not familiar with Python, there is a crash course. Next, there is a Python exercise that you are expected to complete, but if you run into any problems, the solution is also provided.
Then we advanced to data science, beginning with data parsing with Scrapy, followed by data visualizations utilizing a number of Python packages, and lastly learning various data preprocessing approaches. We'll work on a finished project as a team in the end. Following that, we'll study a few traditional and sophisticated machine learning techniques. Some of them will be constructed from scratch, while others will make use of Python's built-in libraries. Every algorithm will have a mini-project at the conclusion. The fundamentals of an artificial neural network and how TensorFlow implements them will be covered, followed by a complete deep learning-based project. Finally, various hyperparameter tuning strategies that will enhance the model's performance will be covered.

Upon completing this course, you'll emerge with a strong foundation in Python programming, data preprocessing, statistical analysis, and machine learning. You'll possess the capabilities to analyze data, build predictive models, and extract meaningful insights that drive informed decisions.