ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
Artificial Intelligence (AI) and machine Learning (ML) is a new, emerging field which consists of a set of tools and techniques used to extract useful information from data. AI & ML is a fast growing discipline and is full of rigorous practical analysis.
Artificial intelligence & Machine Learning have proliferated as businesses use them to process and analyze immense volumes of data, drive better decision-making, generate recommendations and insights in real time, and create accurate forecasts and predictions.
Transforming industry with data science.
How do you learn about the world you are living in using the data we have observed from the world?
Machine learning entails learning about the world we live in using the data we have observed from the world.
Data has become the crude oil in this modern age.
From insight to impact:
insight is something that gives business more clarity about their own reality than they had before. A very good insight can fundamentally change the way a business look at themselves and therefore can empower decision making and impact. But insight is not enough. There is a whole lot of work that is needed to translate insight to impact on the ground and that is where data science can help.
Not only in finding and generating insight about various problems but also on how to translate them into impact.
Learning from data:
Can we learn about the world around us using data?
-take data as input
-find pattern in the data
-summarize the pattern in a mathematical precised way
-Machine learning automates this model building using algorithm pattern.
This data impacts the following industries;
(i) Retail industry and the problem that exist in the industry.
(ii)Health care industry.
(iii) Banking industry etc.
At Dot Matrix Engineering LTD, our complete course in artificial intelligence and machine learning comes in 13 modules.
The module 1 is the fundamental of artificial intelligence and machine learning where we introduce python as an essentials programming language in the tools kit of an artificial intelligence and machine learning professional.
In this course, you will learn the essential of python and its packages for data analysis and computing, including numpy , cypher, panda, seaborn and matplotlib.
Machine learning can be classified into 3 groups;
(i) Supervised learning
(ii) Unsupervised learning
(iii) Reinforced learning
OUR ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING COURSE MODULE
Python is an essential programming language in the tool-kit of an artificial intelligence (AI) & Machine Learning (ML) professional. In this course, you will learn the essential of Python and its packages for Data Analysis and Computing, Including; Numpy, Scipy, Pandas, Seaborn and Matplotlib.
The aim of supervised learning is to build a model that makes predictions based on evidence in the presence of uncertainty. In tis course, you will learn about supervised learning algorithms of linear regression and logistic regression
Ensemble methods help to improve the predictive performance of machine learning models. In this course, you will learn about different ensemble methods that combine several machine learning techniques into one predictive model in other to decrease variance, bias or improve predictions.
Sample Project3Build a model that will help the marketing team of a company to identify potential customers for a term deposit subscription.
Model building is an iterative process, employing feature engineering techniques along with a careful model selection exercise helps to improve the model. Further, tuning the model is an important step to arrive at the best possible result. This module talks about the steps and processes around the same.
Sample project 4.
Perform feature engineering and model Tuning on a model designed to predict the strength of construction materials to enhance accuracy
Unsupervised learning finds hidden patterns or intrinsic structures in data. In this course, you will learn about commonly-used clustering techniques like K-Means clustering and Hierachical clustering along with dimension reduction technique like principal component analysis.
Sample project 5Identify different segments from a banks existing customer pool based on their spending patterns as well as past interactions with the bank
Ensemble methods help to improve the predictive performance of machine learning models. In this course, you will learn about different ensemble methods that combine several machine learning techniques into one predictive model in other to decrease variance, bias or improve predictions.
The module will reflect on the ability of a computer system to see and make sense of visuals using CNN (Convolutional Neural Network). It will enable you efficiently handle image data for the purpose of feeding into CNNs.
Sample project 7Build a Convolutional Neural Network from scratch to classify images into their respective categories.
This module talks about yet another interesting implementation of Neural Networks that revolves around equipping computers to understand human language. You will learn to understand sentiments from texts.
Sample project 8
Detect sentiment from headlines/reviews using the different textual analysis techniques and sentiment analysis.
Gain an understanding of what ChatGpt is and how it works. Dive into the implications of chatgpt for work, business and education. Additionally, learn about prompt engineering and how it can be used to fine-tune outputs for specific use cases.
sample project 9Build a prototype chatbot application using the same models that power chatgpt
Dive into the development stack of chatgpt by learning the mathematical fundamentals that underlie generative AI further learn about transformer models and how they are used in generative AI for natural language
sample project 10
dive deep into an insurance company dataset to find valuable insights on customer profiles based on several statistical tests.
Statistical learning is a branch of applied statistics that deals with Machine Learning, emphasizing statistical models and assessment of uncertainty. This course on statistics will work as a foundation for the artificial intelligence and machine learning concepts learnt in this program.
Sample project 11
Build your own recommendation system for products on an e-commerce website.
A large number of companies use recommender systems, which are softwares that select products to recommend to individual customers. In this course, you will learn how to produce successful recommender systems that use past product purchase and satisfaction data to make high-quality personalized recommendations.
In this module, we will be talking about the model deployment techniques and techniques around making your model scalable, robust and reproducible.
