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Machine Learning with Python Training (beginner to advanced)
Deep dive into Machine Learning with Python Programming. Implement practical scenarios & a project on Recommender System
What you'll learn?
- Agglomerative Hierarchical clustering and how does it work
- Implementation of Agglomerative Hierarchical Clustering
- What is Clustering and Applications of Clustering
- Multiple Linear Regression and Polynomial Regression
- K-Means Clustering and K-Means Clustering algorithm example
- Implementation of Movie Recommender System
- What is KNN? How does the KNN algorithm work?
- Understand what is ML, need for ML, challenges & application of ML in real-life scenarios
- Collaborative Filtering
- Apply Python for Machine Learning programs
- Components of Python ML Ecosystem
- Deep dive into the world of Machine Learning (ML)
- What is Classification, Classification Terminologies in Machine Learning
- What is a Decision Tree and Implementation of Decision Tree
- Logistic Regression
- Association Rule Learning
- Content-based Filtering
- Clustering Algorithms
- Introduction to Recommender Systems
- Apriori algorithm and Implementation of Apriori algorithm
- SVM and its implementation
- Types of Machine Learning
- Woking of Dendrogram in Hierarchical clustering
- scikit-learn Library to implement Simple Linear Regression
- Anaconda, Jupyter Notebook, NumPy, Pandas, Scikit-learn
- Regression analysis
- Hierarchical Clustering
Requirements and What you should know?
- Enthusiasm and determination to make your mark on the world!
Who is this course for?
- Machine Learning Software Engineers & Developers
- AI Specialists & Consultants
- Python Engineers Machine Learning Ai Data Science
- Principal Machine Learning Engineers
- Machine Learning Analysts
- Machine Learning Scientists
- Python Programmers & Developers
- Data Scientists and Senior Data Scientists
- Beginners and newbies aspiring for a career in Data Science and Machine Learning
- Machine Learning Researchers & Enthusiasts
- Computer Vision Machine Learning Engineers
- Anyone interested to learn Data Science, Machine Learning programming through Python
- Data, Analytics, AI Consultants & Analysts
What is this course about?
Machine Learning with Python - Course Syllabus
1. Introduction to Machine Learning
What is Machine Learning?
Need for Machine Learning
Why & When to Make Machines Learn?
Challenges in Machines Learning
Application of Machine Learning
2. Types of Machine Learning
Types of Machine Learning
a) Supervised learning
b) Unsupervised learning
c) Reinforcement learning
Difference between Supervised and Unsupervised learning
Summary
3. Components of Python ML Ecosystem
Using Pre-packaged Python Distribution: Anaconda
Jupyter Notebook
NumPy
Pandas
Scikit-learn
4. Regression Analysis (Part-I)
Regression Analysis
Linear Regression
Examples on Linear Regression
scikit-learn library to implement simple linear regression
5. Regression Analysis (Part-II)
Multiple Linear Regression
Examples on Multiple Linear Regression
Polynomial Regression
Examples on Polynomial Regression
6. Classification (Part-I)
What is Classification
Classification Terminologies in Machine Learning
Types of Learner in Classification
Logistic Regression
Example on Logistic Regression
7. Classification (Part-II)
What is KNN?
How does the KNN algorithm work?
How do you decide the number of neighbors in KNN?
Implementation of KNN classifier
What is a Decision Tree?
Implementation of Decision Tree
SVM and its implementation
8. Clustering (Part-I)
What is Clustering?
Applications of Clustering
Clustering Algorithms
K-Means Clustering
How does K-Means Clustering work?
K-Means Clustering algorithm example
9. Clustering (Part-II)
Hierarchical Clustering
Agglomerative Hierarchical clustering and how does it work
Woking of Dendrogram in Hierarchical clustering
Implementation of Agglomerative Hierarchical Clustering
10. Association Rule Learning
Association Rule Learning
Apriori algorithm
Working of Apriori algorithm
Implementation of Apriori algorithm
11. Recommender Systems
Introduction to Recommender Systems
Content-based Filtering
How Content-based Filtering work
Collaborative Filtering
Implementation of Movie Recommender System
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