Artificial Intelligence Course in Ireland

• Loading the Data
  How to load data from various sources using pandas in Python, loading data from .csv files, loading data from .xlsx files, loading data from web APIs, etc.
• Processing the Data
  Numpy arrays and matrices, multi-dimensional arrays in NumPy, NumPy operations for reading, writing, updating, and deleting arrays, NumPy in-built functions for operations such as merging, concatenating, etc.
• Data Manipulation
  Data frames and series in Pandas, Pandas operations on data frames and series for reading, writing, updating, and deleting values in pandas series and data frames, inbuilt functions in Pandas for concatenation, stacking operations, insertion and deletion operations, etc. Joins and merging operations on Pandas series and data frames.
• Data Visualization
  Understanding the basics of visual plots to assess and analyze data points, various kinds of plots like bar plots, count plots, scatter plots, line plots, etc using matplotlib and seaborn using Python programming.
• Hands-On for Practice
  The EDA case study covers each stage of data processing to gather insights based on the analysis. The data will cover segments from the human resources and FMCG domains.

• Probability Basics
  Introduction to probability, why use probability for artificial intelligence, understanding events and probabilities, introduction to conditions probability, conditional probability, and Bayes theorem
• Descriptive Statistics for Summary
  What is descriptive statistics, what are mean, median, and mode, what significance does the measure of central tendency give, what are variance, and standard deviation, what significance do measures of spread give, how do you measure the skewness in the data, what are range and other measures to describe data
• Inferential Statistics for Evidence-Based Analysis
  Introduction to inferential statistics, sampling, p-value, alpha value, types of errors, hypothesis testing, one-tailed tests, two-tailed tests, one-sample tests, two-sample tests, hypothesis tests for mean, variance, etc. Feature selection, feature engineering, and feature scaling techniques are used to prepare data for training.
• Hands-On for Practice
  Advanced evidence-based statistical findings on sales and marketing data, where evidence-based findings can substantiate plans, inventory management, and other contingency planning to increase efficiency and revenue.

• Introduction to Machine Learning
  What is machine learning in simple words, what are the applications and use cases of machine learning, how are industries using machine learning in their processes and workflows, what is the process of creating a machine learning model, what is the machine learning life cycle, real-life examples of machine learning implementation
• Supervised Learning vs. Unsupervised Learning
  What is supervised learning and unsupervised learning, how do supervised and unsupervised learning work, what are the major differences between supervised and unsupervised machine learning, what are various applications of supervised and unsupervised learning
• Classification Using Machine Learning
  What is classification, Introduction to classification problems in machine learning, machine learning algorithms for classification problems such as logistic regression, decision tree, random forest, etc. metrics to measure the efficiency of classification models such as classification report, and hyperparameter tuning to optimize solutions
• Regression Using Machine Learning
  What is regression, introduction to regression problems in machine learning, machine learning algorithms for regression problems such as linear regression, decision trees, random forest, etc. hyperparameter tuning on regression models, evaluation metrics to check the efficacy of regression models such as MSE (mean squared error), MAPE  (mean absolute percentage error), MAE (mean absolute error), etc.
• Clustering Using Machine Learning
  What are clustering problems in machine learning, machine learning algorithms for clustering such as K-Means clustering, hierarchical clustering, etc. Evaluation metrics such as silhouette score, inter and intra-cluster variance, etc. Hyperparameter tuning on clustering problems.
• Forecasting Using Machine Learning
  Forecasting in machine learning, what are time series, various components of time series such as trends, seasonality, stationarity, etc. Time series forecasting using AR, MA, ARMA, ARIMA, and seasonal ARIMA models.
• Advanced Machine Learning Algorithms
  Introduction to advanced machine learning algorithms such as – XGBoost algorithm, AdaBoost algorithm, Gradient Tree Boosting algorithm, Bootstrap Aggregation algorithm, Ordinary Least Square OLS algorithm,  Markov Chains, Naive Bayes algorithm, Stochastic Gradient Descent algorithm, Gaussian Mixture Model GMM, Singular Value Decomposition SVD (recommendation engines)

• Introduction to Deep Learning
  What is deep learning, the deep learning project life cycle, real-life applications of deep learning, the future of deep learning, ML vs. DL, etc. Introduction to TensorFlow, setup, and installation.
• Neural Networks in Deep Learning
  Single-cell perceptrons, What is a neural network, how does a neural network work, applications of neural networks, fully connected neural networks, how to train neural networks, how to evaluate neural networks, what are weights and biases, what are activation functions, what are layers in a neural network, and building a neural network from scratch using NumPy. Introduction to Keras and sequential modeling.

• Convolutional Neural Networks
  Why use CNN or convolutional neural networks, significance and usage of Convolutional neural networks, the curse of flattening, the Math of Filter/ Kernel, Reduction, Pooling layers, Batch Normalization & Dropout. Process of augmentation, testing the neural network models, learning from the pre-built models, transfer learning, VGG16, fine-tuning, etc.
• Recurrent Neural Networks
  What is context, introduction to Recurrent Neural Networks, understanding RNN and how it works, usage and applications of RNN, working and implementation of RNN, forecasting problems with RNN, text data, and RNN.
• Image Classification
  What is image classification, how to perform image classification, implementation of classification model using image data, introduction to object detection, using pre-trained model weights for image classification, etc.
• Text Classification
  Introduction to text processing and classification using deep neural networks; usage and implementation of text classification using TensorFlow; sentiment analysis using deep neural networks, etc.

• LSTM
  Introduction to LSTM, applications of LSTM, why use LSTM, and working and implementation of LSTM.
• Transformers
  What are transformers, why do we use transformers, How do transformers work, implementation of transformers, transformer Encoder-decoder architecture, self-attention, etc
• BERT
  What are language models, how does a language model work, what is BERT, an introduction to working on BERT, how can you use BERT, etc.
• GPT
  What are generative pre-trained models, how can you fine-tune GPTs for your applications, real-life examples and applications of GPTs, how does a GPT work, etc.
• LLM
  What are large language models, how is LLM different than BERT, how do you use an LLM, what are the limitations and advantages of LLM, LLM implementation, using LLM on custom data, etc

• Introduction to Prompt Engineering
  What is prompt engineering, the science behind prompt engineering, usage and implementation of prompt engineering, prompt engineering hacks, etc
• Prompt Engineering for Developers
  ChatGPT in development, using Bito AI Code Assistant for development, ChatGPT for front end and back end
• Prompt Engineering for E-commerce
  ChatGPT to generate leads, ChatGPT to qualify leads, ChatGPT to close deals, creating engaging product descriptions, personalizing your customer experience, and boosting your e-commerce sales.

The artificial intelligence capstone project will assess your approach to solving complex business problems using deep neural networks. You should perform the following in your project:

1. Identify the problem and the approach to solving it using deep learning networks. 
2. Data processing and feature engineering for the problem. 
3. Training of the neural network, input layers, output layers, etc
4. Evaluation and optimization of the deep learning model based on the efficiency of the predictions made by the model.

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