This course will increase your skills to crack the data science or machine learning interview. You will cover all the most common data science and ML concepts coupled with relevant interview questions.
You will start by covering Python basics as well as the most widely used algorithms and data structures. From there, you will move on to more advanced topics like feature engineering, unsupervised learning, as well as neural networks and deep learning.
This course takes a non-traditional approach to interview prep, in that it focuses on data science fundamentals instead of open-ended questions.
In all, this course will get you ready for data science interviews. By the time you finish this course, you will have reviewed all the major concepts in data science and will have a good idea of what interview questions you can expect.
This course will increase your skills to crack the data science or machine learning interview. You will cover all the most commo...Show More
Learning Roadmap
1.
Are You Ready to Become a Data Scientist?
Are You Ready to Become a Data Scientist?
Learn how to use data science principles, processes, and advancements for solving real-world problems.
2.
Python Basics
Python Basics
Look at Python basics, including variables, decision-making, loops, functions, lists, dictionaries, and classes.
3.
Python Libraries
Python Libraries
6 Lessons
6 Lessons
Examine essential Python libraries for data science, from NumPy to TensorFlow.
4.
More Data Science Tools
More Data Science Tools
7 Lessons
7 Lessons
Grasp the fundamentals of essential data science tools for analysis, visualization, and model deployment.
5.
Data Structures and Algorithms - I
Data Structures and Algorithms - I
7 Lessons
7 Lessons
Deepen your knowledge of essential data structures and algorithms for effective problem-solving.
6.
Data Structures and Algorithms - II
Data Structures and Algorithms - II
4 Lessons
4 Lessons
Follow the process of solving problems with Greedy, Divide and Conquer, Backtracking, and Dynamic Programming.
7.
Statistics and Probability
Statistics and Probability
6 Lessons
6 Lessons
Piece together the parts of statistics, correlation, probability, random variables, and distributions.
8.
Feature Engineering
Feature Engineering
6 Lessons
6 Lessons
Try out feature engineering techniques to enhance data quality and improve model performance.
9.
Basics of Machine Learning
Basics of Machine Learning
3 Lessons
3 Lessons
Unpack the core of machine learning problems, model performance evaluation, and enhancement strategies.
10.
Regression
Regression
5 Lessons
5 Lessons
Break apart regression principles, from simple to nonparametric, while mastering model assessment metrics.
11.
Classification
Classification
7 Lessons
7 Lessons
Find out about classification techniques, including linear classifiers, logistic regression, and decision trees.
12.
Unsupervised Learning
Unsupervised Learning
4 Lessons
4 Lessons
Take a closer look at various clustering methods and nearest neighbor search in unsupervised learning.
13.
Advanced Topics in Machine Learning
Advanced Topics in Machine Learning
4 Lessons
4 Lessons
Tackle Neural Networks, Deep Learning issues, Recommendation Engines, and Natural Language Processing advancements.
14.
Conclusion
Conclusion
2 Lessons
2 Lessons
Master the steps to begin your data science career, with emphasis on continuous learning.
Certificate of Completion
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Course Author:
Developed by MAANG Engineers
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AI Learner Hub
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Every Educative resource is designed by our team of ex-MAANG software engineers and PhD computer science educators — subject matter experts who've shipped production code at scale and taught the theory behind it. No videos, no fluff.
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