What is Data Science & Machine Learning (Theory+Projects)A-Z 90 HOURS course?
The Data Science & Machine Learning (Theory+Projects)A-Z 90 HOURS course is a comprehensive course designed to teach the most in-demand skills in the real world. This course aims to equip students with all the necessary knowledge, concepts, and methodologies of Data Science and Machine Learning with regard to Python. In this course, students will learn statistics for Data Science, Machine Learning for Data Science, Deep Learning for Data Science, and much more.
This course is aimed at beginners and includes hands-on training in data science and machine learning. Students will get practical, coding-based knowledge, designed to take them from a beginner level to an advanced level of understanding. As the course progresses, students will not only gain a solid theoretical foundation but also acquire practical, hands-on experience in Data Science and Machine Learning.
The course is designed to teach data modeling and data warehousing to data science specialists with a combination of algorithms, applications, principles, and tools to make sense of random data clusters.
What the course covers
Python for Data Science and Data Analysis
In the first section of the course, learners start with problem-solving and finish with fancy indexing and plots in Matplotlib. No prior knowledge in any computer science language is assumed. The section covers:
- A general understanding of Python language
- Data science packages such as NumPy, Pandas, Matplotlib, Seaborn, and Sklearn
- Programming in general
Data Understanding and Data Visualization with Python
The second section deals with the in-depth treatment of data science packages both for data manipulation as well as data visualization. The section covers:
- Data visualization using Seaborn, Bokeh, Plotly and Folium
- 2D and 3D visualizations, interactive visualizations, and geographic maps
- Effective data manipulation practices
- Data analysis, data mining, and data manipulation
- Core data science packages including NumPy, Pandas, Matplotlib, Seaborn, Bokeh, Plotly, and Folium
Mastering Probability and Statistics in Python
The third section is focused on probability and statistics, providing a reliable foundation for classical concepts in data science as well as more recent generalizations. The section covers:
- Probability, inference and estimations
- World-famous machine learning techniques with conditional probability
- Deep neural networks as a probability function
- The importance of understanding Probability and Statistics for data science
Machine Learning Crash Course
Machine learning is a branch of artificial intelligence that focuses on developing algorithms that learn from data. As such, the fourth section is dedicated to learning about the concepts in Machine Learning and focuses on all the theoretical as well as practical concepts. The section includes:
- Core concepts of machine learning
- Machine learning pipelines
- Sklearn Python package
- More advanced areas of machine learning
Feature Engineering and Dimensionality Reduction with Python
Data preparation for machine learning models is an essential aspect of data science. The fifth section is focused on data preparation and provides an understanding of why selecting features and transforming features is crucial. The section covers:
- Understanding data preprocessing and data preparation
- Selecting and transforming features to match the machine learning model
- Practical issues with real data
- Performance improvements both in terms of execution time and accuracy of the model
- The required mathematical background for data preparation
Artificial Neural Networks with Python
The sixth section is dedicated to Artificial Neural Networks (ANNs), which can approximate almost any pattern in data. The section is designed to provide an in-depth understanding of ANNs and includes:
- The utility of ANNs in data science
- ANNs ability to understand patterns in data
- Advanced techniques for working with large amounts of data
What Makes The Course Unique?
The Data Science & Machine Learning (Theory+Projects)A-Z 90 HOURS course is unique in that it provides an in-depth understanding of both data science and machine learning concepts and techniques. Furthermore, the course is carefully structured, so each subsequent section builds upon the previous one.
The course is designed to teach students practical skills using a hands-on, learning-by-doing approach. At the end of every subsection, students are assigned homework, exercises, and activities to reinforce their learning. Many of these assessment tasks are coding-based, as the aim is to get students to proceed with implementations.
The course is extremely comprehensive and covers various topics in both data science and machine learning in great detail. The course is easy to understand and descriptive, with live coding and recent models and breakthroughs by the champions in the AI universe.
Course Reviews
The course has an aggregate rating of 4.18627, with over 403 reviews. Students have provided positive reviews praising the course's comprehensive nature, the way the course is structured, and the in-depth understanding they have gained after completing the course.
Many reviewers noted that the course is designed to teach students practical knowledge, making it easy to use what they have learned to address real-world problems, making it an all-around excellent investment for anyone looking to become a Data Science professional.
Is The Course Suitable for You?
The Data Science & Machine Learning (Theory+Projects)A-Z 90 HOURS course is tailored for beginners and advanced learners alike and is designed to provide a solid theoretical foundation as well as practical, hands-on experience.
If you are looking for a comprehensive course that covers all the essential concepts you need to excel as a Data Science professional, this is the course for you. The course is thoughtfully
