Name: Machine Learning & Python Data Science for Business and AI
Availability: InStock

What You'll Learn

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

Requirements

No prior experience with machine learning, data science, or statistics is necessary.

Anyone curious about how machine learning works and how it's shaping the future of business and technology.
Students and recent graduates seeking to build a strong portfolio and land their first job in the AI industry.
Business professionals who want to use data to make smarter decisions and gain a competitive edge.
Developers and programmers who want to expand their skill set and transition into a high growth field.

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17 Courses

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