This Machine Learning with Python course dives into the basics of Machine Learning using Python, an approachable and well-known programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Machine learning is the kind of programming that gives computers the capability to automatically learn from data without being explicitly programmed. This means in other words that these programs change their behavior by learning from data. It is a type of Artificial Intelligence (AI), that provides computers with the ability to learn without being explicitly programmed. In order to follow along with the series, we suggest the participants possess at least a basic understanding of Python. You may follow the Core Python course, offered by us.
We will cover various aspects of Machine Learning, based on Python, in this course.
- Learn Machine Learning basics using Python programming language.
- Get hands-on experience by implementing various Machine Learning algorithms.
- How to classify various types of Machine Learning - Supervised and Unsupervised?
- How does Machine Learning affect society in different ways?
- Training on specific core areas of the selected topic.
- Real-time implementations through practical sessions.
- Well-equipped practical classes where a student can comfortably work on their projects.
- Experienced & dedicated training professionals.
- Understand how to solve Classification and Regression problems in Machine Learning.
- You can transform your theoretical knowledge into practical skills.
- Explore many algorithms and models.
- Improve and enhance your machine learning model’s accuracy through feature engineering.
- Gain essential skills required to penetrate the Industry.
All Courses Idea
Introduction to Python
- Introduction to Python
- NumPy Basics
- Pandas Basics
- Matplotlib basics
Introduction to Machine Learning
- Applications of Machine Learning
- Supervised vs Unsupervised Learning
- Python libraries suitable for Machine Learning
- Data Preprocessing
- Creating validation rules
- Linear Regression
- Non-linear Regression
- Model evaluation methods
- K-Nearest Neighbors
- Decision Trees
- Logistic Regression
- Support Vector Machines
- Model Evaluation
- Introduction to Decision Tree
- Training and Visualizing a Decision Tree
- Visualizing Boundary
- Tree Regression, Regularization and Over Fitting
- End to End Modeling
- K-Means Clustering
- Hierarchical Clustering
- Density-Based Clustering
- Content-based recommender systems
- Collaborative Filtering
Knowledge of Core Python is essentially required.