Course Overview
AI, ML, and Data Science are transforming industries by enabling data-driven decision-making, automation, and intelligent insights. Artificial Intelligence (AI) refers to systems that mimic human intelligence, while Machine Learning (ML), a subset of AI, focuses on algorithms that learn from data. Data Science involves extracting insights from structured and unstructured data using statistical methods, ML models, and visualization techniques. It includes data preprocessing, feature engineering, supervised and unsupervised learning, deep learning, and model evaluation. These technologies power applications like predictive analytics, recommendation systems, natural language processing (NLP), and computer vision, driving advancements in healthcare, finance, eCommerce, and more.
Prerequisites
Skills Covered :
Course syllabus
The outlined subjects are pivotal areas that necessitate comprehensive learning and substantial practice to instill a profound sense of proficiency. Mastery in these topics is integral to cultivating a robust skill set essential for backend development, fostering confidence and expertise in handling intricate backend systems and functionalities.
Linear Algebra (Vectors, Matrices, Eigenvalues/Eigenvectors - Basic Concepts)
Calculus (Derivatives, Integrals - Basic Concepts)
Probability and Statistics
NumPy: Arrays, Array Operations, Indexing, Slicing, Broadcasting
NumPy: Data Manipulation, Reshaping, Boolean Indexing
Pandas: DataFrames, Series, Data Loading/Saving
Pandas: Data Cleaning (Handling Missing Values, Duplicates)
Pandas: Data Manipulation (Filtering, Sorting, Grouping, Aggregation)
Pandas: Data Visualization (Basic Plots with Pandas)
Matplotlib: Basic Plots (Line, Bar, Scatter, Histograms)
Matplotlib: Customizing Plots (Labels, Titles, Legends, Styles)
Seaborn: Statistical Plots (Distributions, Relationships)
Seaborn: Customizing Seaborn Plots (Themes, Palettes)
Introduction to Machine Learning
Supervised, Unsupervised, Reinforcement Learning
Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
Regression: Linear Regression, Decision Tree Regression, Random Forest Regression
Classification: Logistic Regression, Decision Tree Classification, Random Forest Classification, SVM, KNN
Clustering: K-Means, Hierarchical Clustering
Dimensionality Reduction: PCA
Model Evaluation & Validation
Encoding Categorical Variables
Feature Selection Techniques
Introduction to Deep Learning
Neural Networks (Basic Structure, Activation Functions)
Forward and Backward Propagation (Conceptual)
Loss Functions (MSE, Cross-Entropy)
Optimizers (Gradient Descent, Adam)
Activation Functions (ReLU, Sigmoid, Softmax, Tanh)
Regularization (Dropout, L2 Regularization)
PyTorch: Basic Neural Network Creation, Data Loading, Model Training
Neural Network Architectures
Fully Connected Networks: Dense Layers
Convolutional Neural Networks (CNNs): Image Classification
Recurrent Neural Networks (RNNs): Introduction, LSTMs, GRUs (Optional)
Using Pretrained Models (ResNet, VGG, MobileNet)
Fine-tuning for Specific Tasks
Introduction to Natural Language Processing (NLP)
Text Preprocessing (Tokenization, Stemming, Lemmatization)
Language Models (N-grams)
Large Language Models (LLMs)
Applications of LLMs (Text Generation, Translation, Chatbots)
Knowledge Representation and Reasoning
Rule-Based Systems (Optional)
Designing effective prompts for LLMs
Prompt chaining, few-shot prompting, etc.
Retrieval Augmented Generation (RAG)
RAG systems and their importance
Retrieval mechanisms (keyword search, semantic search)
Role of vector databases in RAG
Working with vector databases (Pinecone, Weaviate, etc.)
Using LangChain for building LLM applications
Integrating LangChain with LLMs, vector databases, and other tools
Building Full-Stack AI Applications
Introduction to AI Application Development
Leveraging ChatGPT and Gemini APIs
Developing AI-powered features (chatbots, semantic search, content generation, personalised recommendations)
Deploying full-stack AI applications
