The Introduction of AI and Machine Learning with Python

Learn Data Science, Machine Learning (Artificial Intelligence), Deep Learning & more from the absolute basics!
What you'll learn
Define and understand the meaning of AI and machine learning and explore their applications
Handling Data Frames by learning various tasks including (data exploration, visualization and cleaning)
Understand and create various Supervised Learning algorithms
Understand and create various Unsupervised Learning algorithms
Understand and build recommendation systems
Understand and create NLP (Natural Language Processing) systems
Define and understand Deep Learning in computer vision
Requirements
Previous programming knowledge. Python recommended.
Description
Dive into the concept of Artificial Intelligence and Machine Learning (ML) and learn how to implement advanced algorithms to solve real-world problems. This course will teach you the workflow of ML projects from data pre-processing to advanced model design and testing.
By the end of the course the students will be able to
- Build a variety of AI systems and models.
- Determine the framework in which AI may function, including interactions with users and environments.
- Extract information from text automatically using concepts and methods from natural language processing (NLP).
- Implement deep learning models in Python using TensorFlow and Keras and train them with real-world datasets.
Detailed course outline
Introduction to AI
. Introduction to AI and Machine Learning.
. Overview on Fields of AI
. Computer Vision.
. Natural Language Processing (NLP).
. Recommendation Systems.
. Robotics.
. Project: Creation of Chatbot using traditional programming (Python revision).
Understanding AI
· Understanding how AI works.
· Overview of Machine Learning and Deep Learning.
· Workflow of AI Projects.
· Differentiating arguments vs parameters.
· Project: Implementing functions using python programming (Python revision).
Introduction to Data Science
· Introduction to Data Science.
· Types of Data.
· Overview of DataFrame.
· Project: Handling DataFrame using python programming by learning various tasks including
. Importing Dataset
. Data Exploration
. Data Visualization
. Data Cleaning
Machine Learning
· Overview on Machine Learning Algorithms with examples.
· Types of Machine Learning
. Supervised
. Unsupervised
. Reinforcement
· Types of Supervised Learning
. Classification
. Regression
· Project: Training and deploying machine learning model to predict salary of future candidates using python programming.
Supervised Learning - Regression
· Understanding Boxplot and features of Boxplot function.
· Understanding Training and Testing Data with train_test_split function.
· Project: Creating a machine learning model to solve a regression problem of predicting weight by training and testing data using python programming.
Supervised Learning - Binary Classification
· Understanding Binary Classification problems.
· Overview on Decision tree Algorithm.
· Overview on Random Forest Algorithm.
· Use of Confusion Matrix to check performance of the classification model.
· Project: Implementing Decision tree and Random forest algorithm using python programming to train a classification model to predict diabetic patients, and using confusion matrix to check performance of both algorithms.
Supervised Learning - Multi-class Classification
· Understanding Multi-class Classification problems.
· One-vs-One method.
· One-vs-Many method.
· Project: Implementing Logistic Regression algorithm with both One-vs-One and One-vs-Rest approach to solve a multi-class classification problem of Iris flower prediction. Also, evaluating performance of both approaches using confusion matrix.
Unsupervised Learning - Clustering
· Understanding Unsupervised Learning.
· Use of Unsupervised learning.
· Types of Unsupervised learning
. Clustering
. Association
· Working of KMeans Algorithm.
· Use of Elbow method to determine K value.
· Project: Standardising the data and implementing KMeans algorithm to form clusters in the dataset using python programming.
Unsupervised Learning - Customer Segmentation
· Understanding Customer Segmentation.
· Types of characteristics used for segmentation.
· Concept of Targeting.
· Project: Implementing KMeans algorithm to segment customers into different clusters and analysing the clusters to find the appropriate target customers.
Unsupervised Learning - Association Rule Mining.
· Understanding Association problems.
· Market Basket Analysis.
· Working of Apriori Algorithm.
· Key metrics to evaluate association rules
. Support
. Confidence
. Lift
· Steps involved in finding Association Rules.
· Project: Implement Apriori algorithm to generate association rules for Market Basket Analysis using python programming.
Recommendation System - Content-Based
· Understanding Recommendation Systems.
· Working of Recommendation Systems.
· Types of Recommendation Systems
. Content-based
. Collaborative
· Project: Building a content-based recommendation system using K Nearest Neighbour(KNN) algorithm to recommend a car to the customer based on their input of preferred car features.
Recommendation System - Collaborative Filtering
· Understanding Collaborative filtering technique.
· Types of approaches in collaborative filtering
. User-based
. Item-based
· Project: Building a movie recommendation system using item-based collaborative filtering based on data from a movie rating matrix.
Natural Language Processing - Sentiment Analysis
· Natural Language Processing (NLP)
· Applications of NLP
· Fundamental NLP tasks.
· Tokenization
· Project: Creating a machine learning model that can predict the sentiment in a sentence (Application of NLP).
Deep Learning - Computer Vision
· Understanding Deep Learning.
· Neural Networks and Deep Neural Networks.
· Image Processing
· Project: A neural network model is created for image recognition purposes to predict the digit written in images of hand-written digits.
Image Classification- Bonus Class
· Learn about pre-trained models.
· ResNet50 model trained using ImageNet data.
· Project: Use ResNet50 model to classify images (predicting what the image represents).
Who this course is for
Beginner Python coders curious about AI and machine learning
Any passionate person who is interested in learning AI and data science
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