Learn Python Programming, Data Analysis, and Machine Learning Techniques to Solve Real World Business Challenges with AI
Sub Category
- Data Science
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Objectives
- Overview of Python and Its Ecosystem for Data Science
- Key Python Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn
- Data Structures in Python: Lists, Tuples, Dictionaries, Sets
- Reading and Writing Data Using Pandas (CSV, Excel, JSON, etc.)
- Data Manipulation: Merging, Filtering, Sorting, and Aggregating Data
- Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
- Data Visualization Using Matplotlib and Seaborn
- Handling Missing Data (imputation, deletion, etc.)
- What is Machine Learning? Types of Machine Learning: Supervised vs Unsupervised Learning
- Overview of Machine Learning Algorithms
- Understanding the Data Pipeline in Machine Learning
- Linear Regression: Theory, Implementation, and Evaluation
- Logistic Regression: Theory, Implementation, and Evaluation
- K-Nearest Neighbors (KNN) Algorithm
- K-Means Clustering: Theory, Implementation, and Evaluation
- Hierarchical Clustering
- Feature Selection Methods: Filter Methods, Wrapper Methods, Embedded Methods
- Bagging: Random Forests and Bootstrap Aggregating
- Boosting: AdaBoost, Gradient Boosting, XGBoost
- Introduction to Neural Networks and Deep Learning
- Structure of a Neural Network: Neurons, Layers, Weights, Biases
Pre Requisites
- No prior experience with machine learning, data science, or statistics is necessary.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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