AI & ML w/ Python-2022-Practical Hands On with Minimum Maths

AI & ML w/ Python-2022-Practical Hands On with Minimum Maths, available at $59.99, has an average rating of 4.45, with 55 lectures, based on 55 reviews, and has 260 subscribers.

You will learn about Develop an ML Model using Python, Perform Error Analysis and Make Predictions. Develop your first ML Model – 'Hello World' for AI and ML. Develop your first 'complete ML project'. Understand basics of Machine Learning. Classifiers and Models in ML. Learn Supervised, Unsupervised, Regression, Classification, Clustering in ML. Learn RMSE method, Confusion matrix, Classification report in ML. Top Python libraries for Machine Learning. SK-learn library for ML with Python. Project 1: Complete ML project of IRIS flower dataset. Project 2: Complete ML project of Digit recognition system. This course is ideal for individuals who are Beginners with no or less experience with programming and curious for Data science. or Beginners curious for AI (Artificial Intelligence) and ML (Machine Learning). or Corporate professionals who wants to understand basic development in AI and ML. or Trainers and teachers who wants a start in Artificial Intelligene. or Engineering students who wants to learn AI and ML. It is particularly useful for Beginners with no or less experience with programming and curious for Data science. or Beginners curious for AI (Artificial Intelligence) and ML (Machine Learning). or Corporate professionals who wants to understand basic development in AI and ML. or Trainers and teachers who wants a start in Artificial Intelligene. or Engineering students who wants to learn AI and ML.

Enroll now: AI & ML w/ Python-2022-Practical Hands On with Minimum Maths

Summary

Title: AI & ML w/ Python-2022-Practical Hands On with Minimum Maths

Price: $59.99

Average Rating: 4.45

Number of Lectures: 55

Number of Published Lectures: 53

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 53

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Develop an ML Model using Python, Perform Error Analysis and Make Predictions.
Develop your first ML Model – 'Hello World' for AI and ML.
Develop your first 'complete ML project'. Understand basics of Machine Learning.
Classifiers and Models in ML.
Learn Supervised, Unsupervised, Regression, Classification, Clustering in ML.
Learn RMSE method, Confusion matrix, Classification report in ML.
Top Python libraries for Machine Learning.
SK-learn library for ML with Python.
Project 1: Complete ML project of IRIS flower dataset.
Project 2: Complete ML project of Digit recognition system.

Who Should Attend

Beginners with no or less experience with programming and curious for Data science.
Beginners curious for AI (Artificial Intelligence) and ML (Machine Learning).
Corporate professionals who wants to understand basic development in AI and ML.
Trainers and teachers who wants a start in Artificial Intelligene.
Engineering students who wants to learn AI and ML.

Target Audiences

Beginners with no or less experience with programming and curious for Data science.
Beginners curious for AI (Artificial Intelligence) and ML (Machine Learning).
Corporate professionals who wants to understand basic development in AI and ML.
Trainers and teachers who wants a start in Artificial Intelligene.
Engineering students who wants to learn AI and ML.

[ 90% of the Course has been updated and rest will be updated soon. Enroll Now!!! ]

Artificial Intelligence and Machine Learning doesn’t have to be hard and complex if we approach it in right way. Sometimes, we need to have a complete working model to understand the basic concept. And this course is going to deliver you the same.

Course objective:

The sole objective of this course is to get you introduced with AI (Artificial Intelligence) and ML (Machine Learning). All the programs and projects that we are going to develop, are using Python programming language. So, You need Python knowledge.

If you are not familiar with Python programming language, You can take our FREE course on Python. [This free course of Python is also getting updated.]

Learning outcomes:

After completing this course and assignments given to you, you will have:

Multiple programs developed for Machine Learning
Multiple complete projects developed for Machine Learning
A complete ML model developed

For concept seeker in you, you shall be able to answer these questions comfortably:

What is AI and What is ML?
How AI and ML are different but related?
What are DL, NLP, ANN, DNN etc.?
What is Anaconda, Spyder, Jupyter etc. and how and why do we use them for Machine Learning?
What are classifiers and models in ML?
How to develop programs and projects of Machine Learning?

For the developer in you, you shall be comfortable with:

Machine learning development environment with Python.

Course Curriculum

Chapter 1: Basics of Artificial Intelligence

Lecture 1: About the course

Lecture 2: Artificial Intelligence in general

Lecture 3: Types of Intelligences

Lecture 4: Definitions of AI

Lecture 5: AI is more ART than Science

Lecture 6: Dartmouth conference

Chapter 2: Basics of Machine Learning

Lecture 1: Machine Learning and AI

Lecture 2: Natural Language Processing and AI

Lecture 3: Deep Learning and ML

Lecture 4: Artificial Neural Network, Deep NN and DL

Chapter 3: AI applications – A look

Lecture 1: A live AI – Teachable machine

Lecture 2: Do-It-Yourself

Chapter 4: Basics of Python3 (Bonus section)

Lecture 1: Enroll in our FREE course on Python

Chapter 5: Python libraries for ML

Lecture 1: Modules, Packages and Libraries

Lecture 2: Python Libraries for ML

Chapter 6: Anaconda development environment

Lecture 1: Anaconda distribution package

Lecture 2: Anaconda package installation

Lecture 3: Anaconda, Jupyter and Spider

Chapter 7: Your first ML Model

Lecture 1: Classifiers and Models

Lecture 2: Elements of an ML program

Lecture 3: Your first ML program

Lecture 4: Detailed discussion on ML program

Lecture 5: Do-It-Yourself

Lecture 6: A quick recap

Chapter 8: Core ML concepts

Lecture 1: Types of datasets in ML

Lecture 2: Supervised and Unsupervised learnings

Lecture 3: Regression, Classification and Clustering Models

Lecture 4: Mathematical representation of ML models

Chapter 9: Model Evaluation Techniques

Lecture 1: Model evaluation methods in general

Lecture 2: RMSE method for Regression

Lecture 3: Confusion matrix for Classification

Chapter 10: Your first complete ML project

Lecture 1: Developing complete ML project – understanding data set

Lecture 2: Developing complete ML project – understanding flow of project

Lecture 3: Developing complete ML project – visualizing data set through Python

Lecture 4: Developing complete ML project – development

Lecture 5: Developing complete ML project – concepts explanations

Lecture 6: Do-It-Yourself

Lecture 7: What's next?

Lecture 8: Congratulations !!!

Lecture 9: Feedback.

Chapter 11: Knowledge Bytes

Lecture 1: Knowledge Byte 1 – Where comes the AI exactly?

Lecture 2: Knowledge Byte 2 – What is a proper definition for AI?

Lecture 3: Knowledge Byte 3 – What do we mean by flower of AI?

Lecture 4: Knowledge Byte 4 – Why AI finds it's applications everywhere?

Lecture 5: Knowledge Byte 5 – What is ML and Non-ML approach?

Lecture 6: Knowledge Byte 6 – What's the exact diff bw AI and ML?

Lecture 7: Knowledge Byte 7 – What's the difference between ML and DL?

Lecture 8: Knowledge Byte 8 – What exactly is DL?

Lecture 9: Knowledge Byte 9 – AI, ML and DL. What's the difference?

Lecture 10: Knowledge Byte 10 – ANN, DNN, DL, ML & AI – What connects?

Lecture 11: Knowledge Byte 11 – What happens (exactly) in an ML progam?

Lecture 12: Knowledge Byte 12 – Module, Package & Library – A look?

Lecture 13: Knowledge Byte 13 – 'A kind of' complete Python Library for ML?

Instructors

Aalekh Rai
Founder @ KARD | Sr. Technical Trainer

Rating Distribution

1 stars: 2 votes
2 stars: 3 votes
3 stars: 16 votes
4 stars: 13 votes
5 stars: 21 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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