Data Science & Machine Learning with GenAI

Descriptive Statistics

• Descriptive Statistics
• Central Tendency
  • Mean
  • Median
  • Mode
• Measure of Spread
  • Deviation
  • Standard Deviation
  • Variance
• Frequency Distribution
• Quartiles Deviation
• Visualize Data Distributions with different kinds:
  • Normal
  • Skewed
  • Kurtosis

Inferential Statistics

• Hypothesis Testing
  • p-value
  • Confidence Intervals
  • Chi-Square Test
• Probability Theory
  • Bayes’ Theorem
  • Probability Distributions

Linear Algebra for ML

• Matrices & Vectors
  • Transpose
  • Inverse
  • Determinants
• Eigenvalues, Eigenvectors (Will be used in PCA & Dimensionality Reduction)

Data Engineering & Preprocessing

• Extracting Data
  • APIs
    • Understanding of API concepts
    • HTTP and Different API methods
    • Explore Google API, Weather API, Stock APIs etc

• • Web Scraping
  • Database
  • Cloud
  • Online Resources
    • Kaggle
    • Google Dataset Search

• Data Cleaning & Transformation (using Pandas, Numpy)
  • Working with Outliers
  • Data Skewness
  • Handling None/Empty Values
  • Data Transformation

Exploratory Data Analysis (EDA)

• Finding Insights from data using Pandas library by applying:
  • Working with 1D and 2D data
  • Aggregate Functions
  • Data Filtering
  • Indexing in DataFrame
  • Merging multiple datasets
  • Group By
  • Sorting data on basis of index and values
  • Applying multiple mathematical operations
• Feature Engineering
• Text to number conversion using encoding techniques
  • One-Hot Encoding
  • Label Encoding
• Data Scaling

Data Visualization

• Visualize data graphically using libraries
  • Pandas
  • Seaborn
  • Matplotlib
  • Plotly
• Building live graphs on real time data using animation
• Building Interactive Dashboards with Streamlit

Introduction to Machine Learning

• What is Algorithm?
• Understanding Machine Learning Concepts
• How ML programming is different from standard programming?
• Different types of ML algorithms
• Online and Offline learning
• Understanding the concepts of training/testing data

Supervised Learning

• Regression Models
  • Single and Multi Linear Regression Algorithm
  • Polynomial Regression
• Classification Models
  • Decision Trees
  • Random Forest
  • SVM
  • Naïve Bayes
  • KNN
  • Logistic Regression
• Evaluation Metrics
  • Confusion Matrix
  • ROC-AUC
  • Precision
  • Recall
  • F1 Score
  • Classification Report
  • Accuracy and R2 Score

Unsupervised Learning

• Clustering Algorithms
  • K-Means
  • Hierarchical Clustering
• Dimensionality Reduction
  • PCA
  • LDA
  • Autoencoders

Model Optimization & Deployment – Tune models for best results and deployment for real time usage.

• Hyperparameter Tuning
  • Grid Search
  • K-Fold
• Model Deployment
  • Flask
  • Pickle
  • Joblib
• Gradient Descent Algorithm

Text Preprocessing & Feature Engineering

• Apply different NLP techniques using NLTK library
  • Tokenization
  • Stemming
  • Lemmatization
• Word Embeddings Concepts
  • One – Hot Encoding
  • TF-IDF
  • Word2Vec

Building NLP Models

• Sentiment Analysis
• Document Classification
• Named Entity Recognition (NER)

Neural Network Fundamentals

• Stochastic Gradient Descent Algorithm
• Activation Functions
  • ReLU
  • Sigmoid
  • Softmax
• Backpropagation
• Neurons
• Weights
• Biases and Variance
• Hidden Layers

Building & Training Neural Networks

• Using TensorFlow & Keras build and train models
• Loss Functions
• Optimization Techniques

Convolutional Neural Networks (CNNs)

• Create CNN model with different number layers
• Image Classification model
• Transfer Learning Concepts
  • ResNet
  • VGG
  • MobileNet

Recurrent Neural Networks (RNNs) & Transformers

• Understanding embedding concept
• Create RNN model with different number of layers
• Understanding concept of Transformers
  • Encoders
  • Decoders
• BERT & GPT for NLP

• Introduction to Generative AI
• Importance of GenAI
• Architecture of GenAI
• Concepts of Encoders
  • GANs
  • VAEs
• Understanding of Large Language Models and Its Working
• Implementing different LLM models using OpenSource API

Fine-Tuning Large Language Models

• Adapting GPT & BERT for Custom Use Cases
• Fine-Tuning with Hugging Face

Retrieval-Augmented Generation (RAG)

• Enhancing LLMs with Real-Time Data Retrieval
• Implementing Chunking/Splitting
• Embeddings
• Vector Database & Vector Search using
  • FAISS
  • Pinecone
  • ChromaDB

Building AI-Powered Assistants

• Real-World AI Chatbots & Applications

• Introduction to Cloud Computing
  • What is cloud and its benefits
  • Key concepts like IaaS, PaaS, SaaS
• Popular Cloud Platforms (AWS, Azure, GCP)
  • Overview of major cloud providers and their ML/data tools.
• Storage and Compute Services (like S3, Blob)
  • Use virtual machines and scalable compute engines for data processing.
• Cloud Databases
  • Access cloud-based databases (SQL, NoSQL).
• Machine Learning Services
  • Use tools like Azure ML Studio, AWS SageMaker, and Vertex AI to train and deploy models.
  • Drag-and-drop ML workspace to
    • Build
    • Train
    • Optimize
    • Deploy models
• Integrate Python scripts and prebuilt modules
• Model versioning, tracking, and performance analysis
• Supports real-time and batch inference