13 Modules
2 - 3 MonthsOverview
Course Description
This machine learning course is structured for both machine learning for beginners and seasoned tech experts. Dive into supervised and unsupervised learning, neural networks, and advanced model optimization with the best machine learning course content available. Experience live machine learning classes, and unlock personalized learning paths with our machine learning online course and certification options.
What you'll learn
- Core principles of machine learning
- Data preparation and feature engineering
- Building and evaluating classification & regression models
- Neural networks and deep learning introduction
- Deploying machine learning models in production
- Real-world machine learning projects
Course Features:
- Beginner-friendly and advanced modules
- Machine learning certification and project portfolio
- Recorded lectures and live Q&A sessions
- Peer-to-peer collaboration and support
- Flexible learning with self-paced content
Course Content
Installation of Anaconda Prompt
Jupyter Notebook-An Overview
Shorcut Lkeys in Jupyter Notebook
Data Types in Python
Rules for Naming the Variables List, Tuple, Set, Dictionary
"Introduction to Files and directories"
Introduction to the command prompt or terminal paths
Text files Reading from a text file Opening a file using `with`
If, else if and else condition
For and While Loop
Machine Learning Libraries
Numpy-Hands on
"Pandas-Hands on"
Learn how to explore, visualize, and extract insights from data
Data Visualization
Matplotlib-Hands on
Seaborn hands on
You need to think statistically and to speak
the language of your data
Measures of Central Tendency
Measures of Dispersion
"IQR Statistics-Hands-On"
Classification, Regression, Fine-tuning your model
Supervised Learning
Unsupervised Learning
Linear Regression
Metrics in Linear Regression Hands-on in Linear Regression
Logistic Regression
Metrics in Logistic Regression
Hands-on in Logistic Regression
Linear regression
Metrics for Linear regression
Introduction to Data Preprocessing
Standardizing Data
Exploratory Data Analysis
Missing Values Outliers
"Standardization Mnormalization Feature Scaling and Selection"
Decision Tree
Bagging
Boosting Random Forest
Neural Network & Project using data science packages,analysis, visualization, create model, extract pure data etc
