AI and Machine Learning Training

Artificial Intelligence and Machine Learning let computers learn from data to make predictions, recognise images, understand language and generate content. AI skills are now valued in almost every industry. This course at Aptech Fatehabad starts with Python and data handling, builds the maths and statistics you need, then covers machine learning with scikit-learn, deep learning with neural networks, natural language processing and Generative AI with large language models. You complete hands-on projects and a capstone, and learn to turn models into simple web apps.

AI and Machine Learning Training Course Content

Python for AI

•     Python refresher
•     Jupyter Notebook and Google Colab
•     Functions and OOP
•     NumPy arrays
•     Pandas DataFrames
•     Data cleaning and preprocessing

Maths and Statistics for ML

•     Descriptive statistics
•     Probability and distributions
•     Linear algebra: vectors and matrices
•     Gradients and optimisation (intuition)
•     Hypothesis testing

Data Visualisation and EDA

•     Matplotlib
•     Seaborn
•     Exploratory data analysis
•     Feature engineering
•     Handling missing values and outliers

Machine Learning

•     Types of ML: supervised, unsupervised, reinforcement
•     scikit-learn workflow
•     Linear and logistic regression
•     Decision trees and random forest
•     KNN, SVM and Naive Bayes
•     Gradient boosting (XGBoost)
•     Train/test split and cross-validation
•     Model evaluation metrics
•     Hyperparameter tuning

Unsupervised Learning

•     K-Means clustering
•     Hierarchical clustering
•     PCA dimensionality reduction
•     Recommendation systems

Deep Learning

•     Neural networks and backpropagation
•     TensorFlow / Keras and PyTorch
•     CNNs for image classification
•     RNNs and LSTMs
•     Transfer learning

NLP and Generative AI

•     Text preprocessing and tokenisation
•     Word embeddings
•     Transformers and Large Language Models
•     Prompt engineering
•     Using LLM APIs
•     Retrieval-Augmented Generation (RAG)
•     Responsible AI and ethics

Deployment and Projects

•     Saving and loading models
•     Building AI apps with Streamlit and Flask
•     Model deployment basics
•     Capstone project
•     Portfolio on GitHub and Kaggle