Description
Machine Studying with Python – Course Syllabus
1. Introduction to Machine Studying
- What’s Machine Studying?
- Want for Machine Studying
- Why & When to Make Machines Be taught?
- Challenges in Machines Studying
- Utility of Machine Studying
2. Varieties of Machine Studying
- Varieties of Machine Studying
a) Supervised studying
b) Unsupervised studying
c) Reinforcement studying
- Distinction between Supervised and Unsupervised studying
- Abstract
3. Parts of Python ML Ecosystem
- Utilizing Pre-packaged Python Distribution: Anaconda
- Jupyter Pocket book
- NumPy
- Pandas
- Scikit-learn
4. Regression Evaluation (Half-I)
- Regression Evaluation
- Linear Regression
- Examples on Linear Regression
- scikit-learn library to implement easy linear regression
5. Regression Evaluation (Half-II)
- A number of Linear Regression
- Examples on A number of Linear Regression
- Polynomial Regression
- Examples on Polynomial Regression
6. Classification (Half-I)
- What’s Classification
- Classification Terminologies in Machine Studying
- Varieties of Learner in Classification
- Logistic Regression
- Instance on Logistic Regression
7. Classification (Half-II)
- What’s KNN?
- How does the KNN algorithm work?
- How do you determine the variety of neighbors in KNN?
- Implementation of KNN classifier
- What’s a Resolution Tree?
- Implementation of Resolution Tree
- SVM and its implementation
8. Clustering (Half-I)
- What’s Clustering?
- Functions of Clustering
- Clustering Algorithms
- Okay-Means Clustering
- How does Okay-Means Clustering work?
- Okay-Means Clustering algorithm instance
9. Clustering (Half-II)
- Hierarchical Clustering
- Agglomerative Hierarchical clustering and the way does it work
- Woking of Dendrogram in Hierarchical clustering
- Implementation of Agglomerative Hierarchical Clustering
10. Affiliation Rule Studying
- Affiliation Rule Studying
- Apriori algorithm
- Working of Apriori algorithm
- Implementation of Apriori algorithm
11. Recommender Methods
- Introduction to Recommender Methods
- Content material-based Filtering
- How Content material-based Filtering work
- Collaborative Filtering
- Implementation of Film Recommender System
Who this course is for:
- Knowledge Scientists and Senior Knowledge Scientists
- Machine Studying Scientists
- Python Programmers & Builders
- Machine Studying Software program Engineers & Builders
- Pc Imaginative and prescient Machine Studying Engineers
- Rookies and newbies aspiring for a profession in Knowledge Science and Machine Studying
- Principal Machine Studying Engineers
- Machine Studying Researchers & Fanatics
- Anybody to study Knowledge Science, Machine Studying programming by Python
- AI Specialists & Consultants
- Python Engineers Machine Studying Ai Knowledge Science
- Knowledge, Analytics, AI Consultants & Analysts
- Machine Studying Analysts
Necessities
- Enthusiasm and dedication to make your mark on the world!
