Artificial Intelligence Engineer Course in Columbia

The Simplilearn-IBM co-developed Artificial Intelligence course combines AI, data science, ML, and deep learning to deploy state-of-the-art instruments and models in the real world. With this Artificial Intelligence Course in Columbia you gain the fundamental artificial intelligence principles, need to advance in the field, which cover python programming and data science fundamentals, and a practical understanding of machine learning statistics.

In this Artificial Intelligence Course in Columbia, you will learn how to use Python libraries such as NumPy, SciPy, Scikit-learning, and essential machine learning principles including artificial neural networks, data abstraction layers, and TensorFlow.

Artificial Intelligence and Machine Learning will be applied in various mainstream industries by the end of 2025. From healthcare, insurance, transport, to improving customer services, fintech startups and logistics, Artificial Intelligence will become ubiquitous, shaping the years 2025-2030 and beyond.

Therefore, securing a job profile in this domain can benefit you immensely. However, an artificial intelligence course in Columbia can help you advance your abilities while keeping you updated with any new developments in the field.

At the end of the Artificial Intelligence Course In Columbia, you will be proficient in the following:

• Understanding topics like machine learning, deep learning, and neural networks, as well as the definition of AI, its applications, and use cases.
• Creating AI chatbots and virtual assistants without writing a single line of code, then deploy them on a website.
• Creating AI-powered solutions with minimal code using APIs and Python at the end of our Artificial Intelligence Course In Columbia
• Designing and building intelligent agents that can help to design and develop new-age Artificial Intelligence-led creations like learning models, captivating games, and applications solve customer satisfaction issues, impacted by logical constraints, knowledge-based support systems, probabilistic models, and functions to help with agent decision-making.
• Mastering ideas of supervised and unsupervised models of learning, including linear regression, logistic regression, clustering, size reduction, K-NN, and pipelines, engine recommendations, and modeling time series
• Understanding the fundamentals, key functions, operations, and the implementation pipeline of TensorFlow
• Understanding Artificial Intelligence sophisticated themes such as coevolutionary neural networks, recurrent neural networks, deep networking training, and high-level interfaces

So doing this AI Course In Columbia can be very beneficial.

Including over 15 real-life brand initiatives in various fields, this Artificial Intelligence course in Columbia is co-developed by IBM. The projects entailed in the course are intended to master critical topics such as supervised and unsupervised learning, reinforcement learning, vector support, deep learning and tensor flux, neural networks, convolution neural networks, and recurrent neural networks.

This AI course in Columbia includes a project Capstone that allows you to revisit the topics learned during the entire duration of the course. You will take special mentoring workshops to build a high-quality industrial project and solve problems in the real world. The Capstone Project covers crucial areas, from analyzing exploratory data through design and fit to evaluating stock trends for various leading companies.

In order to successfully complete this Capstone project, you will need to use today's most cutting-edge and widely-used supervised algorithms as well unmonitored algorithms used in the artificial intelligence field like Regression, Multinomial Naïve Bayes, NLP, SVM and many others. Not only will you be issued the capstone credential after successfully submitting the project, but you will also have a project that may be presented as a testimonial to your learning to prospective employers.

Project 1: Uber Fare Provision | Domain: Delivery (Commerce)

Uber, one of the biggest taxi operators in the United States, wants to make the fare forecast for each journey more accurate. Be a helping hand in designing the proper model.

Project 2: Mercedes-Benz Test Bench Time Reduction | Domain: Cars

Mercedes-Benz, a multinational automotive manufacturer, based in Germany, seeks to minimize its test time for any car. More rapid testing will shorten market hitting time. Build and optimize the algorithm via reducing dimensionality and several approaches, including XGBoost.

Project 3: Amazon product rating | Domain: e-commerce

One of today's biggest US-based businesses in the e-commerce industry, Amazon offers very accurate suggestions to customers for their requirements matched with their business type and popular reviews offered for the same category of products. Amazon wants to improve the recommendation engine by anticipating ratings for unreviewed products and adding them to suggestions.

Project 4: Walmart demand prediction | Domain: sales

Forecast exact sales for 45 Walmart shops, one of the major retail stores in the United States, considering the influence of promotional events. Check for impact on sales on macroeconomic parameters such as CPI, unemployment rate, etc.

Project 5: Customer experience improvement for Comcast | Domain: Telecommunications

One of the leading telecom companies based out of USA, Comcast is looking to take its customer experience to the next level by getting to the source of the issue and taking steps which will eliminate problem areas affecting customer satisfaction. The organization is also searching for essential recommendations to provide the most satisfactory experience for its customers.

Project 6: Analysis of attrition for IBM | Domain: Workforce Analytics

You will be required to uncover characteristics that affect employee attrition for IBM, one of the largest US-based IT businesses. The organization would like to develop a logistic regression model based on identified parameters to forecast whether an employee churns or not.

Project 7: Columbia 311 Service Request Analysis | Domain: Telecommunication Service Request Analysis

Conduct call analysis for Columbia City's 311 service request line. You will focus on the techniques of data dispute to analyze data patterns and view the primary forms of complaints.

Project 8: MobieLens Analysis data set | Domain: engineering

The Research Project GroupLens is a research group of the University of Minnesota, Department of Computer Science and Engineering. The group's researchers take part in various research projects in the fields of information filtering, collaborative filtering, and system recommendation. We ask you to analyze user datasets by deploying Exploratory Data Analysis techniques and related strategies.

Project 9: Analysis of stock data | Domain: stock market

In this project, you can import data from Yahoo, Apple, Amazon, Microsoft, Google, and other leading companies utilizing the Yahoo data reader. You will be required to conduct basic analytics, including charting, closing prices, volume trading, completing daily return analyses, and using pair tracks to indicate the correlation between stocks.

This Simplilearn-offered Artificial Intelligence Course In Columbia is a perfect program for career advancement in a variety of professions and job disciplines, particularly due to the rising demand and application of artificial intelligence across industries like:

• Developers who wish to land a job as an artificial intelligence engineer
• Analytics Managers who lead an analyst team can take this Artificial Intelligence Course In Columbia
• Information Architects wanting to acquire a deeper understanding of AI.
• Professional analysis specialists who desire to kickstart their careers in Machine Learning
• Students who seek to build an artificial intelligence and machine learning profession will benefit from AI courses in Columbia

This Artificial Intelligence Course In Columbia should be attended by participants with:

• Mastery of Python programming's essential principles
• Basic statistical knowledge