Machine Learning Using Python
Published 5/2024
Duration: 3h54m | .MP4 1280x720, 30 fps® | AAC, 44100 Hz, 2ch | 2.03 GB
Genre: eLearning | Language: English

Machine Learning Using Python

What you'll learn
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 k-nearest neighb
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.

Requirements
Python Programming

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 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.
Who this course is for:
Beginners and all those who are intrested to learn Machine learning and pursue career in it.

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