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Beginner Guide to Artificial Intelligence & Machine Learning

Name: Beginner Guide to Artificial Intelligence & Machine Learning
Rating: 4.4 (7 reviews)

An Insight and comprehensive overview of the concepts of AI and ML

Created byPhani Avagaddi

Last updated 11/2024

English

English [Auto],

What you'll learn

Introduction to Artificial Intelligence
Introduction to Machine Learning
Understanding the Roles available in AI career path
Planning your career building path in AI and ML specific to the roles
Build a portfolio of work to have on your resume
Supervised and Unsupervised Learning
Learn which Machine Learning model to choose for each type of problem
Learn how to program in Python using the latest Python 3
Learn to pre process data, clean data, and analyze large data

Course content

4 sections • 8 lectures • 16h 14m total length

Introduction2:03:54
What is Artificial Intelligence and Machine Learning?1:54:32
Explore the fundamentals of artificial intelligence and machine learning, including supervised, unsupervised, and reinforcement learning, deep learning, neural networks, transfer learning, and key ethical and privacy considerations.

Supervised Learning1:56:33
Master supervised learning foundations, from data pre-processing and feature engineering to CNN and RNN architectures. Apply model fitting, prediction, and evaluation on prepared data with encoding, scaling, and train-test splits.
Unsupervised Learning and Deep Learning Overview1:59:02
Explore the shift from shallow perceptrons to deep neural networks, including unsupervised learning, multilayer perceptrons and CNNs, mastering convolution, pooling, activation, and feature extraction for image and language tasks.

Deep Learning Fundamentals2:15:22
Explore deep learning fundamentals, including forward and backward propagation, gradient descent, loss, CNNs, and RNNs. Learn applications in computer vision, NLP, speech, and generative AI such as Transformers and rag.
Model Evaluation and Validation2:02:22
Understand how generative AI and large language models use transformers, tokens, and embeddings to generate text, and how prompt engineering, few-shot prompts, and fine-tuning reduce hallucinations.

Generative AI and Large Language Models1:55:47
Explore generative AI and large language models through hands-on GitHub profiles, transformers, Hugging Face, and RAG with embeddings. Learn to build, fine-tune, and deploy ML models using Streamlit or FastAPI.
RAG and Capstone Projects Overview2:07:16
Discover practical ai and machine learning workflows, from python basics and data prep to model training with scikit-learn, culminating in rag-based capstone projects.

Requirements

Beginners from IT or Non IT any one can watch these videos
AI Enthusiasts who wants to enter into the AI world
Career shifting people who want to know AI and ML
Whoever wants to understand the foundational concepts can watch these sessions

Description

Implement Machine Learning algorithms

How to improve your Machine Learning Models

Build a portfolio of work to have on your resume

Supervised and Unsupervised Learning

Explore large datasets using data visualization tools like Matplotlib

Learn NumPy and how it is used in Machine Learning

Learn to use the popular library Scikit-learn in your projects

Learn to perform Classification and Regression modelling

Master Machine Learning and use it on the job

Learn which Machine Learning model to choose for each type of problem

Learn best practices when it comes to Data Science Workflow

Learn how to program in Python using the latest Python 3

Learn to pre process data, clean data, and analyze large data.

Developer Environment setup for Data Science and Machine Learning

A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided

Real life case studies and projects to understand how things are done in the real world

Guidance to choose your career path based on your background and build the path in next 6 months

Comprehensive understanding on foundational concepts like Neurons, Perceptron, Multilayer Perceptron, Transformers.

Good overview on Convolution Neural Networks and Recurrent Neural Networks

Who this course is for:

Fresh Graduates, IT professionals shifting their career to AI path, Non IT domain experties

Beginner Guide to Artificial Intelligence & Machine Learning

What you'll learn

Explore related topics

Course content

AIML Foundation - Introduction2 lectures • 3hr 58min

Supervised Learning and Unsupervised Learning2 lectures • 3hr 56min

Deep Learning Fundaments and Model Building2 lectures • 4hr 18min

GenAI , RAG and capstone projects2 lectures • 4hr 3min

Requirements

Description

Who this course is for: