Download to read offline
Uploaded byMRUNALINI
Machine learning in python course contents
This document outlines a 20-day training course on machine learning in Python. The course covers topics such as Python basics, data structures, Pandas, NumPy, visualization, linear algebra, probability and statistics, regression, classification algorithms like logistic regression, naive Bayes, and SVM, clustering, association rule learning, dimensionality reduction, and SQL. The course is divided into 4 weeks covering Python fundamentals, mathematics, supervised learning algorithms, and unsupervised learning algorithms. Each day focuses on a different machine learning concept and its implementation in Python.
Educationâ—¦
More Related Content
![Large Scale Image Forensics using Tika and Tensorflow [ICMR MFSec 2017]](https://cdn.slidesharecdn.com/ss_thumbnails/largescaleimageforensics-icmrmfsec2017-170608221906-thumbnail.jpg?width=640&height=640&fit=bounds)
Similar to Machine learning in python course contents
Machine learning in python course contents
- 1. Asst. Prof. MRUNALINIK (Statistics) Statistics / Mathematics / R / Python / Trainer Email : mrunalini0107@gmail.com Machine Learning in Python CONTENTS Asst. Prof. MRUNALINI K (Statistics) Email: mrunalini0107@gmail.com
- 2. Day 1 –Why Machine Learning Day 2 – Python Day 3 – Data Structures Day 4 – Pandas Day 5 – NumPy Day 6 – Visualization Day 7 – Linear Algebra Day 8– Probability & Statistics Day 9 – Introduction to Regression Day 10 – Introduction to Classification Day 11 – Machine Learning Lifecycle Day 12 – Logistic Regression Day 13 –Naive Bayes Day 14 – Support Vector Machine–SVM Day 15 – Decision Trees Day 16 – Ensemble Methods Day 17 – Clustering Day 18 – Association Rule Learning Day 19 – Dimensionality Reduction Day 20 – Just enough SQL
- 3. Week 1 –Python Day 1 – Why Machine Learning • Join the revolution • R vs Python • Machine Learning Demo • Traditional Programming vs ML • Machine Learning Path Day 2 – Python • Environment Setup • Python Setup • Hello world in Python • Python IDE setup • Python Basics • Number guessing game • Average grade Program • Prime number Program • Reverse string program • Date and time • Where are Python libraries • Number guessing game – Random •Challenges Day 3 – Data Structures • Python Lists • Python Dictionary • Python Sets • Python Tuples • Challenges Asst. Prof. MRUNALINI K (Statistics) Email: mrunalini0107@gmail.com
- 4. Day 5 –NumPy • NumPy • n-dimensional Array • Array Operations • Array Indexing and Slicing • Challenges • Additional Reading • meshgrid Day 4 – Pandas • Pandas • What is Pandas • Dataframe • Create Dataframe • Display Dataframe • Select data from Dataframe • Add/delete Rows • Add/delete Columns • Grouping data • Merging Dataframes Day 6 – Visualization • Matplotlib • What is Matplotlib • Plot Object • Lifecycle of a Plot • More Plots Week 1 – Python Asst. Prof. MRUNALINI K (Statistics) Email: mrunalini0107@gmail.com
- 5. Week 2 –Mathematics Day 7 – Linear Algebra • Linear Algebra • Linear Algebra Basics • Dot Product • Miscellaneous Topics Day 9 – Introduction to Regression • Linear Regression • Multi-linear Regression • Correlation • Hypothesis Tests ( p-value ) • Key Parameters • r-squared • r-squared adjusted • RMSE • Feature Selection • Polynomial Regression • Over fitting • Assumptions Day 8 – Probability & Statistics • Probability & Statistics • Key Terms • What is a Distribution • Central Limit Theorem • Correlation Asst. Prof. MRUNALINI K (Statistics) Email: mrunalini0107@gmail.com
- 6. Day 11 –Machine Learning Lifecycle • What is ML Lifecycle • Data Ingestion • Data Import • Data Preprocessing • Imputation of Missing Values • Data Modeling • Model Accuracy • Validation Day 10 – Introduction to Classification • What is Classification • K – Nearest Neighbors • KNN in Python • Confusion Matrix • KNN Regression • Feature Scaling • Effect of Outliers • Parameter Tuning • Key features of KNN • Assumptions Week 2 – Mathematics Asst. Prof. MRUNALINI K (Statistics) Email: mrunalini0107@gmail.com
- 7. Week 3 –Supervised Machine Learning Algorithms Day 12 – Logistic Regression • What is Logistic Regression • Math behind Logistic Regression • Implementation • Optimization • Evaluation • ROC Curve • Area under ROC Curve Day 14 – Support Vector Machine–SVM • What is SVM • Support Vectors • Kernels • Hyperplane • Performance Tuning • cost • kernel • gamma • SVM for Regression Day 13 –Naive Bayes • What is Naive Bayes • Bayes Theorem & Conditional Probability • Naive Bayes Theorem • Naive Bayes by hand • Classify fruits • Classify Messages as Spam or Ham • Naive Bayes on Continuous Variables Asst. Prof. MRUNALINI K (Statistics) Email: mrunalini0107@gmail.com
- 8. Week 3 –Supervised Machine Learning Algorithms Day 15 – Decision Trees • What are Decision Trees • Implementation • Visualization • Gini Index • Decision Trees for Regression • Overfitting • Pruning • Tree Depth • Impurity Day 16 – Ensemble Methods • What is an Ensemble • Types of Ensemble Methods • Bagging Implementation • Bagging • Random Forest • Boosting Implementation • AdaBoost • Gradient Boost Day 17 – Clustering • What is Clustering • K-means Clustering • Optimum k value Asst. Prof. MRUNALINI K (Statistics) Email: mrunalini0107@gmail.com
- 9. Week 4 –Unsupervised Machine Learning Algorithms Day 18 – Association Rule Learning • What is Associative Rule Learning • Key Terms • Support • Confidence • Lift • Apriori Algorithm Day 20 – Just enough SQL • SQL • What is a database • What is SQL • SQL Connectors • Load data • Selecting data • SELECT Statement • Aggregate Functions • Table JOINs Day 19 – Dimensionality Reduction • What is Dimensionality Reduction • Hughes Phenomonon • Curse of Dimensionality • The Solution • Principal Component Analysis • Mean & Variance • Eigen Vectors & Eigen Values Asst. Prof. MRUNALINI K (Statistics) Email: mrunalini0107@gmail.com
- 10. LEARN SOMETHING… NEW EVERYDAY….!!! Asst. Prof. MRUNALINI K (Statistics) Email: mrunalini0107@gmail.com