Content
1.
Are You Ready to Become a Data Scientist?
3 Lessons
Learn how to use data science principles, processes, and advancements for solving real-world problems.
2.
Python Basics
8 Lessons
Look at Python basics, including variables, decision-making, loops, functions, lists, dictionaries, and classes.
3.
Python Libraries
6 Lessons
Examine essential Python libraries for data science, from NumPy to TensorFlow.
4.
More Data Science Tools
7 Lessons
Grasp the fundamentals of essential data science tools for analysis, visualization, and model deployment.
5.
Data Structures and Algorithms - I
7 Lessons
Deepen your knowledge of essential data structures and algorithms for effective problem-solving.
6.
Data Structures and Algorithms - II
4 Lessons
Follow the process of solving problems with Greedy, Divide and Conquer, Backtracking, and Dynamic Programming.
7.
Statistics and Probability
6 Lessons
Piece together the parts of statistics, correlation, probability, random variables, and distributions.
8.
Feature Engineering
6 Lessons
Try out feature engineering techniques to enhance data quality and improve model performance.
9.
Basics of Machine Learning
3 Lessons
Unpack the core of machine learning problems, model performance evaluation, and enhancement strategies.
10.
Regression
5 Lessons
Break apart regression principles, from simple to nonparametric, while mastering model assessment metrics.
11.
Classification
7 Lessons
Find out about classification techniques, including linear classifiers, logistic regression, and decision trees.
12.
Unsupervised Learning
4 Lessons
Take a closer look at various clustering methods and nearest neighbor search in unsupervised learning.
13.
Advanced Topics in Machine Learning
4 Lessons
Tackle Neural Networks, Deep Learning issues, Recommendation Engines, and Natural Language Processing advancements.
14.
Conclusion
2 Lessons
Master the steps to begin your data science career, with emphasis on continuous learning.
Certificate of Completion
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