Applied Artificial Intelligence and ML for Business Applications

Unlock an AI Career breakthrough with Zero Coding Experience!

Experience the simplicity of AI through Live Coding Sessions for all.

About the Program

In a world flooded with online courses, finding one that transcends theory and immerses you in real-time, hands-on experiences is a rare gem. Introducing our cutting-edge online course on Applied Artificial Intelligence and ML for Business Applications – a program designed not just to impart knowledge, but to empower you with practical skills that make a difference.

What to Expect?

Imagine creating your own GPT for the HR department using Language Learning Models (LLM) or crafting a custom ChatGPT tailored for finance, admissions, or any other department within a company, school, or college. In our program, these aren’t just dreams – they are tasks you'll accomplish in real-time. Engage in live, hands-on AI projects that mirror the challenges and opportunities of the contemporary business landscape.

We are making

HANDS ON AI

simpler for everyone

Format

Real Time Online learning

Course Duration

16 weeks

Time Commitment

2 – 3 hours a week

AI Projects

12+ Business
Algorithms

Personalised Support

Available around the clock

Our Promises

Are Meant To Be Kept.

We expect absolutely Zero Programming Experience from you, because we start from Scratch, i.e. Python.

While AI courses worldwide can cost between $7,000 and $20,000, we're here to make AI education as affordable as possible.

We're proud to offer a 100% Satisfaction Guarantee, complete with doubt-clearing sessions alongside our hands-on coding classes.

And here's our promise: If you're not completely satisfied after two sessions, no strings attached, we'll refund your payment – no hidden conditions, no questions asked.

Live AI
Coding Sessions
0 +
Cheapest
0 x
100%
Satisfaction
0 %
Moneyback
Guarantee
0 %

We want to make AI Simple and Easy for Everyone

What can I Expect?

Clear Conceptual Understanding of AI Algorithms in Plain English and Their
Implementations with

Live AI
Coding Sessions
0 +
Business
AI Algorithms
0 +
Real-world Take home Assignments
0 +
Reusable AI code for Your Business Usecases
0 +

Our Syllabus will keep everyone upskilled in AI

16 Weeks

Online Learning

12+

AI Business Framework

249

Objectives:

  • Install and configure Python and the necessary AI libraries, such as NumPy, Pandas, Matplotlib, and Scikit-Learn.
  • Learn the basics of Linux command-line tools for AI, such as SSH, Git, and Docker.
  • Perform exploratory data analysis on datasets using Python, including data cleaning, visualization, and feature engineering.
  • Understand the basics of statistical concepts relevant to AI, such as probability, distributions, and hypothesis testing.
  • Implement basic AI algorithms in Python, such as linear regression, logistic regression, and decision trees.

Learning Outcomes:

  • Students will be able to set up a Python environment for AI development.
  • Students will be able to use Linux command-line tools for AI.
  • Students will be able to perform exploratory data analysis using Python.
  • Students will be able to explain statistical concepts relevant to AI.
  • Students will be able to implement basic AI algorithms in Python.

Objectives:

  • Learn the basics of supervised learning algorithms such as support vector machines, gradient boosting machines, and random forests.
  • Understand ensemble techniques such as bagging, boosting, and stacking.
  • Implement supervised learning and ensemble algorithms in Python.

Learning Outcomes:

  • Students will be able to explain the basics of supervised learning algorithms.
  • Students will be able to understand ensemble techniques.
  • Students will be able to implement supervised learning and ensemble algorithms in Python.

Objectives:

  • Learn the different techniques for tuning machine learning models, such as cross-validation and hyperparameter optimization.
  • Understand the basics of unsupervised learning algorithms such as k-means clustering and hierarchical clustering.
  • Implement model tuning and unsupervised learning algorithms in Python.

Learning Outcomes:

  • Students will be able to explain the different techniques for tuning machine learning models.
  • Students will be able to understand the basics of unsupervised learning algorithms.
  • Students will be able to implement model tuning and unsupervised learning algorithms in Python.

Objectives:

  • Learn the basics of neural networks and deep learning.
  • Implement basic deep learning models such as perceptrons, convolutional neural networks, and recurrent neural networks using Pytorch or Tensorflow.

Learning Outcomes:

  • Students will be able to explain the basics of neural networks and deep learning.
  • Students will be able to implement basic deep learning models using Pytorch or Tensorflow.

Objectives:

  • Learn the basics of computer vision concepts such as image processing, object detection, and image classification.
  • Implement basic computer vision models using Pytorch or Tensorflow.

Learning Outcomes:

  • Students will be able to explain the basics of computer vision concepts.
  • Students will be able to implement basic computer vision models using Pytorch or Tensorflow.

Objectives:

  • Learn the basics of recommendation systems and natural language processing.
  • Implement basic recommendation systems and natural language processing models using Pytorch or Tensorflow.

Learning Outcomes:

  • Students will be able to explain the basics of recommendation systems and natural language processing.
  • Students will be able to implement basic recommendation systems and natural language processing models using Pytorch or Tensorflow.

Objectives:

  • Learn the basics of generative AI and large language models.
  • Deploy a ChatGPT-like app to the cloud.

Learning Outcomes:

  • Students will be able to explain the basics of generative AI and large language models.
  • Students will be able to deploy a ChatGPT-like app to the cloud.

Objectives:

  • Learn the basics of reinforcement learning.
  • Deploy a simple AI model to the cloud using a cloud platform such as AWS, Azure, or Google Cloud Platform.

Learning Outcomes:

  • Students will be able to explain the basics of reinforcement learning.
  • Students will be able to deploy a simple AI model to the cloud using a cloud platform such.

Career Support

We Build Your
Portfolio

When you're starting anew in a particular field, guidance from an experienced individual within that field can provide you with a valuable head start. We will be helping you secure a job, assist you in the long run by enhancing your strengths, addressing weaknesses, and devising a plan to accomplish your career objectives.

#DreamBig and #AchieveBig 

Resume & LinkedIn
Profile Review:

Get expert guidance to present yourself in the most compelling way, with a comprehensive review of your resume and LinkedIn profile. Showcase your strengths effectively and make a lasting impression on potential employers and professional connections.

Interview Preparation and E-Portfolio Development

Gain valuable insights into recruiters' expectations and enhance your interview skills with an insiders' perspective. Additionally, craft a professional E-portfolio demonstrating your mastery of industry skills and tools, making you stand out in the job market.

Key Facts about

Artificial Intelligence

Our Applied AI and ML course is designed for those who want to integrate AI/ML solutions into their tech infrastructure and learn about advanced AI, ML, and deep learning techniques and their applications.

This program is your stepping stone to leading the implementation of AI in your current role or company, and it can also help you transition into a tech career in AI and ML.

The AI industry is projected to increase twentyfold by 2030, reaching an estimated 2 trillion U.S. dollars.
By 2025, AI has the potential to replace around 300 million full-time jobs, while simultaneously generating over 60 million new jobs by 2022.
By 2030, AI has the potential to contribute up to $15.7 trillion to the global economy, surpassing the current combined output of China and India.
By the end of this course, you will be able to: 

Embark on your journey to AI mastery—where skills meet opportunities and careers reach new heights!

Get a CHANCE to work at:  
FAQ's

AI is transforming various business functions across industries. Here are some key examples:
- Marketing and Sales: AI personalizes marketing campaigns, improves customer segmentation, and automates lead generation.
- Customer Service: Chatbots and virtual assistants provide 24/7 customer support, handle inquiries, and resolve common issues.
- Operations and Logistics: AI optimizes supply chains, predicts demand fluctuations, and automates tasks like scheduling and inventory management.
- Product Development: AI facilitates faster product design cycles, personalizes product recommendations, and predicts customer preferences.
- Risk Management and Fraud Detection: AI analyzes large datasets to identify potential risks, detect fraudulent activities, and improve decision-making.

AI solutions are available at different scales and price points, making them accessible for businesses of all sizes. Here are some examples:
- Small businesses: Can leverage AI chatbots for customer service, online marketing tools with AI-powered recommendations, and cloud-based AI solutions for data analysis.
- Medium-sized businesses: Can explore AI-powered CRM systems, automated marketing campaigns, and predictive maintenance solutions.
- Large enterprises: Can invest in custom AI development for tasks like product development, personalized learning platforms, and advanced fraud detection systems.

AI can be used for data-driven decision-making by:
- Identifying patterns and trends: AI can analyze large datasets to discover hidden patterns and trends that might be missed by humans, providing valuable insights for decision-making.
- Predicting outcomes: AI models can be built to predict the potential outcomes of different decisions, allowing businesses to choose the options with the highest chance of success.
- Optimizing processes: AI can be used to optimize business processes by identifying areas for improvement and suggesting changes that can lead to better efficiency and results.

Here are some steps a business can take to get started with AI:
- Identify areas where AI can add value: Analyze your business processes and identify areas where AI can automate tasks, improve efficiency, or gain insights from data.
- Start small and pilot your first project: Choose a specific problem or process where AI can be applied and test its effectiveness before scaling up.
- Seek expert advice: Consult with AI specialists or solution providers who can help you find the right AI solution for your business needs.

Online businesses can leverage AI in various ways, such as:
- Personalizing the customer experience: AI can personalize product recommendations, website content, and marketing messages based on individual customer behavior and preferences.
- Optimizing pricing and promotions: AI can analyze customer data and market trends to set competitive prices and offer targeted promotions.
- Detecting fraudulent activities: AI can analyze transactions and user behavior to identify and prevent fraudulent transactions.
- Enhancing online advertising: AI can optimize online ad campaigns by targeting the right audience and delivering personalized ads at the right time.

- Data availability and quality: AI models require access to large amounts of clean and high-quality data, which can be a challenge for some businesses.
- Cost of implementation and maintenance: Implementing and maintaining AI solutions can involve initial costs for technology, data, and expertise.
- Ethical considerations: Businesses need to ensure responsible use of AI, considering potential biases and ensuring transparency in decision-making processes.

- The benefits of adopting AI in business include increased efficiency, cost savings, improved decision-making, enhanced productivity, better customer experiences, and the ability to gain actionable insights from large volumes of data.

- Businesses should consider challenges such as data quality and availability, integration with existing systems, privacy and security concerns, regulatory compliance, talent acquisition and training, and the ethical implications of AI-driven decisions.

- Successful AI implementations in business include companies using AI-powered chatbots to enhance customer service, retailers utilizing AI for demand forecasting and inventory management, and financial institutions employing AI for fraud detection and risk assessment.

- Increased adoption of AI across all industries: AI will become more accessible and affordable, leading to broader adoption by businesses of all sizes.
- Focus on explainable AI: There will be an increasing emphasis on developing AI models that are transparent and explainable, addressing concerns about bias and lack of interpretability.
- Rise of hyperautomation: Combining AI with other automation technologies like robotics will further transform business operations.

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