Machine Learning Using Python
"Unleashing Intelligence with Python and Machine Learning"
Development ,Data Science,Machine Learning
Lectures -11
Duration -3 hours
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Course Description
This course serves as an introduction to the field of machine learning with a focus on implementation using Python programming language. Machine learning is a branch of artificial intelligence that enables systems to learn from data and make predictions or decisions without being explicitly programmed. Python has emerged as one of the most popular programming languages for machine learning due to its simplicity, versatility, and a rich ecosystem of libraries such as scikit-learn, Mlxtend, Pandas, Seaborn, SciPy etc.
Throughout this course, students will explore fundamental machine learning concepts, algorithms, and techniques, and gain hands-on experience in implementing them using Python. The course will cover topics including:
1. Introduction to Machine Learning
2. Data Cleaning using Python
· Creating a Data Frame
· Describing the Data
· Navigating Data frames
· Selecting Row Based Conditionals
· Replacing Values
· Renaming Columns
· Finding The Minimum, Maximum. Sum, Average, and Count
· Finding Unique Values
· Handling Missing Values
· Deleting a Column
· Deleting a Row
· Dropping Duplicate rows
· Group Rows by Values and Time
· Looping over a Column
· Applying a Function Over All Elements in a Column
· Applying a Function to Groups
· Concatenating Data Frames
· Merging Data Frames
Handling Numerical Data
· Rescaling a Feature
· Standardizing a Feature
· Transforming Features
· Detecting Outliers
· Handling Outliers
· Deleting Observations with Missing Values
Handling Categorical Data
· Encoding Ordinal Categorical Features
· Encoding Dictionaries of Features
3. Plotting and exploring Numerical Data and Categorical Data
· Box Plot
· Histogram
· Scatterplot
· Cross Tabulations
4. Training and modelling the data
· Splitting a dataset into training and validation sets
· K-fold cross-validation
· Bootstrap Sampling
5. Dimensionality Reduction using Feature Extraction
· Reducing Features using PCA
· Reducing Features using LDA
· Reducing Features using NMF
6. Supervised Algorithms for Classification
· KNN
· Decision Tree
· Random forest
· Support Vector Machine
· Naive Bayes
· Logistic Regression
7. Improving Performance of the Model with Ensembling Methods
· Ada Boost
· XG Boost
8. Evaluating Performance of the Model for Classification
· Confusion Matrix
· Kappa Score
· F – measure
· Accuracy
· Precision
· Recall
· ROC Curve
9. Regression
· Linear Regression
· Logistic Regression
· Evaluation with R2 score
10. Unsupervised Algorithms
Clustering
· K-means
· K-Medoids
· Hierarchical
Association Analysis
· Apriori Algorithm and Association Rules
By the end of this course, students will have a solid understanding of machine learning concepts and techniques, proficiency in implementing machine learning algorithms using Python, and the ability to apply machine learning to solve real-world problems. This course will empower students to pursue further studies or careers in the rapidly growing field of machine learning and artificial intelligence.
Goals
Understand the fundamental concepts of machine learning and its applications across various domains.
Learn the process of data preprocessing, including handling missing data, feature scaling, and encoding categorical variables.
Master a variety of supervised learning algorithms such as linear regression, logistic regression, decision trees, support vector machines, and KNN.
Explore unsupervised learning techniques including clustering, dimensionality reduction, and association rule learning.
Develop the ability to critically analyze and interpret machine learning results and make data-driven decisions.
Build a solid foundation for further studies or career advancement in the field of machine learning and artificial intelligence.
Prerequisites
Knowledge of Python Programming

Curriculum
Check out the detailed breakdown of what’s inside the course
Course Structure
1 Lectures
-
Course Structure 04:26 04:26
Introduction to Machine Learning
1 Lectures

Data Wrangling
1 Lectures

Data Visualization
1 Lectures

Supervised Learning using Python
2 Lectures

Enhancing the Performance with Feature Reduction and Ensembling Techniques
2 Lectures

Enhancing the Performance with K-Fold Cross Validation and Boot Strap Sampling
1 Lectures

Unsupervised Learning
2 Lectures

Instructor Details

Dr. Prerna Agrawal
Myself Dr. Prerna Agrawal a professor with a passion for teaching students and shaping their careers in IT. I have more than 12+ years of teaching experience and have a specialization in Machine Learning, Data Science, Cloud Computing, Big Data, Mobile Technology, Databases etc. The teaching style for programming will be fully based on live practical demo sessions.
Learning with me will be fun, and the outcome will be that the student will be able to use the knowledge gained from real-time problems to obtain solutions.
The methodology used will be presentations, practical demos, practical exercises and case studies to be solved.
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