English | Size: 11.80 GB
Genre: eLearning
What you’ll learn
Deep dive into the world of Machine Learning (ML)
Apply Python for Machine Learning programs
Understand what is ML, need for ML, challenges & application of ML in real-life scenarios
Types of Machine Learning
Components of Python ML Ecosystem
Anaconda, Jupyter Notebook, NumPy, Pandas, Scikit-learn
Regression analysis
scikit-learn Library to implement Simple Linear Regression
Multiple Linear Regression and Polynomial Regression
Logistic Regression
What is Classification, Classification Terminologies in Machine Learning
What is KNN? How does the KNN algorithm work?
What is a Decision Tree and Implementation of Decision Tree
SVM and its implementation
What is Clustering and Applications of Clustering
Clustering Algorithms
K-Means Clustering and K-Means Clustering algorithm example
Hierarchical Clustering
Agglomerative Hierarchical clustering and how does it work
Woking of Dendrogram in Hierarchical clustering
Implementation of Agglomerative Hierarchical Clustering
Association Rule Learning
Apriori algorithm and Implementation of Apriori algorithm
Introduction to Recommender Systems
Content-based Filtering
Collaborative Filtering
Implementation of Movie Recommender System
Who this course is for:
Data Scientists and Senior Data Scientists
Machine Learning Scientists
Python Programmers & Developers
Machine Learning Software Engineers & Developers
Computer Vision Machine Learning Engineers
Beginners and newbies aspiring for a career in Data Science and Machine Learning
Principal Machine Learning Engineers
Machine Learning Researchers & Enthusiasts
Anyone interested to learn Data Science, Machine Learning programming through Python
AI Specialists & Consultants
Python Engineers Machine Learning Ai Data Science
Data, Analytics, AI Consultants & Analysts
Machine Learning Analysts
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