15+ Modules
2 - 3 MonthsOverview
Course Description
The Tek-z artificial intelligence course covers the essential concepts and practical applications of AI, providing you with hands-on experience in real-world projects. Whether you’re new or seeking to enhance your tech career, this ai course delivers in-depth knowledge through interactive modules and live sessions. This artificial intelligence online course is tailored for anyone looking to lead in the ai classes and ai courses online environment.
What you'll learn
- Introduction to the fundamentals of artificial intelligence
- Understanding AI algorithms and machine learning basics
- Hands-on project development using Python and AI libraries
- Applications of AI across industries
- Developing intelligent solutions using neural networks
- Real-world problem solving using AI tools and frameworks
Course Features:
- Expert-led live interactive classes
- Flexible online learning modules
- Lifetime access to course material
- Project-based training for portfolio building
- Industry-recognized certification
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
NLTK
Spacy
Gensim
FastText
Huggingface,How does generative AI work?
Generative AI models
What are Dall-E, ChatGPT and Bard?
What are use cases for generative AI.
