Requirements

Enthusiasm and determination to make your mark on the world!
Description
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 Prepackaged Python Distribution: Anaconda
 Jupyter Notebook
 NumPy
 Pandas
 Scikitlearn
4. Regression Analysis (PartI)
 Regression Analysis
 Linear Regression
 Examples on Linear Regression
 scikitlearn library to implement simple linear regression
5. Regression Analysis (PartII)
 Multiple Linear Regression
 Examples on Multiple Linear Regression
 Polynomial Regression
 Examples on Polynomial Regression
6. Classification (PartI)
 What is Classification
 Classification Terminologies in Machine Learning
 Types of Learner in Classification
 Logistic Regression
 Example on Logistic Regression
7. Classification (PartII)
 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 (PartI)
 What is Clustering?
 Applications of Clustering
 Clustering Algorithms
 KMeans Clustering
 How does KMeans Clustering work?
 KMeans Clustering algorithm example
9. Clustering (PartII)
 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
 Contentbased Filtering
 How Contentbased Filtering work
 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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