Download The Introduction of AI and Machine Learning with Python

What you would learn in The Introduction of AI and Machine Learning with Python course?

Learn about Artificial Intelligence and Machine Learning (ML) concepts and learn to use advanced algorithms to address real-world issues. This course will help you understand the steps involved in ML projects, from data preparation to advanced design of models and testing.

At the end of the course, students will be competent to:

Create a variety of AI models and systems.

Establish the framework in which AI could function, including interactions with humans and the environment.

• Find information in text by using the concepts and techniques of the field of natural language processing (NLP).

Develop deep learning models using Python by using TensorFlow and Keras and train them using real-world data.

The course outline is detailed:

An Introduction to AI

. An introduction to AI as well as Machine Learning.

. A brief overview of Fields of AI:

. Computer Vision.

. Natural Language Processing (NLP).

. Recommendation Systems.

. Robotics.

. Project: Development of Chatbot with the traditional software method (Python version).

Understanding AI

• Understanding the way AI functions.

A brief overview of Machine Learning as well as Deep Learning.

* Process flow of AI Projects.

* Differentiating arguments from parameters.

*Project: Implementing functionalities with Python programmers (Python rev.).

An Introduction to Data Science

"Introduction on Data Science.

Different types of data.

* An overview of DataFrame.

"Project: DataFrame Handling" with Python programming, by learning a variety of tasks like:

. Importing Dataset

. Data Exploration

. Data Visualization

. Data Cleaning

Machine Learning

A brief overview of Machine Learning Algorithms with examples.

* Different types of Machine Learning

. Supervised

. Unsupervised

. Reinforcement

The following are the types of supervised learning:

. Classification

. Regression

* Project to train and deploy machine learning models to predict future salary candidates through Python programming.

Supervised Learning Regression

"Understanding" Boxplot and the functions part of the Boxplot function.

Learning to Understand Training as well as Testing Data with train_test_split function.

* Project: Designing the machine-learning model needed to address the problem of predicting weight through testing and training data with Python programming.

Supervised Learning Binary Classification

1. The understanding of Binary Classification problems.

* An overview of the Decision tree Algorithm.

* A short overview of Random Forest Algorithm.

* Utilization of the Confusion Matrix to evaluate the effectiveness of the classification model.

* Project Making use of The Decision tree and Random Forest algorithm with Python programming to develop a classification model that can identify diabetic patients and use confusion matrix to test the effectiveness for both algorithms.

Supervised Learning Multi-class Classification

* Understanding Multi-class Classification problems.

* One-vs-One method.

* One-vs-Many method.

* Project Implementation of Logistic Regression algorithm using both One-vs-1 and One-vs-Rest approaches to solve the classification problem in multi-classes for Iris flowers prediction. Additionally, evaluate the performance of both methods using a confusion matrix.

Unsupervised Learning - Clustering

"Unsupervised learning: Understanding.

* The use of unsupervised learning.

* Types of unsupervised learning:

. Clustering

. Association

* Work for KMeans Algorithm.

* Utilization to calculate the Elbow technique to calculate the K value.

* Project: Standardizing the data and applying the KMeans algorithm to create clusters within the data set using Python programming.

Unsupervised Learning-Customer Segmentation

* Understanding Customer Segmentation.

* The types of attributes that are used to segment.

• Conceptualization of Targeting.

• Project to implement the KMeans algorithm to divide customers into various clusters and then analyze the clusters to identify the most appropriate customer to target.

Unsupervised Learning Unsupervised Learning Association Rule Mining.

* Understanding Association problems.

* Market Basket Analysis.

* Work of the Apriori Algorithm.

* The most important metrics to judge the rules of association:

. Help

. Confidence

. Lift

* The steps involved in locating Association Rules.

* Project Install Apriori algorithm to generate association rules Market Basket Analysis using python programming.

Recommendation System based on Content

* Understanding Recommendation Systems.

* Working of Recommendation Systems.

* Different types of recommendation systems:

. Content-based

. Collaborative

* Project: Developing an algorithm for a recommendation based on content using K the Nearest Neighbour(KNN) algorithm for recommending a car to the client according to the input of their preferred car features.

Recommendation System - Collaborative Filtering

* Understanding the technique of collaborative filtering.

The various types of methods used in collaborative filtering

. User-based

. Item-based

The project aims to build a film recommendation system using collaborative filtering based on information from the movie rating matrix.

Natural Language Processing - Sentiment Analysis

* Natural Language Processing (NLP)

* Application of NLP

* Fundamental NLP tasks.

* Tokenization

* Project: Building an algorithm for machine learning that can predict an expression's mood (Application for NLP).

Deep Learning - Computer Vision

* Understanding Deep Learning.

* Neural Networks and Deep Neural Networks.

* Image Processing

* Project Neural network models have been designed for image recognition to identify the digits written in handwritten numbers.

Image Classification Bonus Class

Learn about models that have been trained.

* ResNet50 model trained using ImageNet data.

* Project: Make use of the ResNet50 model to categorize images (predicting the image's meaning).

Content of the Course:

• Understand the significance and definition of AI and machine learning, and investigate their potential applications
• Processing Data Frames using different learning tasks like (data exploration visualization, cleaning, and data exploration)
• Create and understand different Supervised Learning algorithms
• Create and understand different Unsupervised Learning algorithms
• Learn and design recommendations systems
• Learn and develop NLP (Natural Language Processing) systems.
• Learn about and define Deep Learning in computer vision

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