Data Science

• Python Introduction 
• Variables 
• Operators 
• Conditional Statements 
• DataTypes: 
  • Int 
  • Float 
  • Complex 
  • String 
  • List 
  • Tuple 
  • Set and FrozenSet 
  • Dict 
  • Bool 
• Loops in Python: 
  • For Loop 
  • While Loop 
  • Functions 
  • Python Functions 
  • Types of Arguments 
  • Recursive Function 
  • Lambda Functions 
  • Built-in Functions 
• Module and Packages: 
  • OS (Operating System) 
  • Glob 
  • Shutil 
  • Time 
  • DateTime 
  • Regular Expressions 
  • Pytesseract
• File Handling 
• Exception Handling 
• Object-Oriented Programming: 
  • Inheritance 
  • Encapsulation 
  • Polymorphism 
  • Abstraction 
• Iterator and Generator 
• Decorator 
• Python Web Framework (Flask)

• Preprocessing and Model Building 
  • Numpy 
  • Pandas 
  • Scipy 
  • Stats model 
  • Sklearn 
  • Nltk 
  • Imblearn 
  • Mlxtend 
• ■Visualization 
  • Matplotlib 
  • Seaborn 
  • plotly(3D Ploting) 

❖ NumPy: (Numerical Python) 

• Introduction to Numpy 
• Datatypes of ndarrays 
• Dealing with ndarrays, copies and views 
• Arithmetic operations, 
• Indexing , Slicing, splitting arrays 
• Shape manipulation 
• Stacking together different data 
• Stats using Numpy 
• Linear Algebric Operations 
• Logarithmic Functions 

❖ Pandas: (Data Analysis) 

• DataFrame and Series 
• DataFrame operations 
• Data Slicing, indexing 
• DataFrame functions 
• Reading the files- csv, excel 
• Storing file in various formats 
• Useful DataFrame functions 
• Stats using pandas 
• Dealing with missing data 
• Operations over the data
• Sorting DataFrame Using Values and index 
• Groupy and Apply 
• Joins in Pandas

❖ Machine Learning Algorithms: 

• Supervised Machine Learning: 
  • Linear Regression 
  • Logistic Regression 
  • K-Nearest Neighbour 
  • Decision Tree 
  • Random Forest 
  • AdaBoost 
  • Support Vector Machine 
  • Naive Bayes Classifier 
  • Gradient Boost 
  • Extreme Gradient Boost 
  • Unsupervised Machine Learning: 
• K-Means Clustering 
• Principal Component Analysis 

❖ Other DS Topics: 

• Preprocessing: 
• Handling of Missing Values 
• Handling of Outliers: 
  • Detection(Zscore, IQR) 
  • Handling (Imputation, Transformation) 
• Encoding(Label Encosing, One Hot Encoding) 
• Feature scaling(Normalization,Standardization) 
• Binning 
• Feature Selection: 
• Filter Method 
• Wrapper Method 
• Embedded Method 
• Hypothesis Testing: 
• P-value 
• Z Test 
• T-Test 
• ANOVA 
• Chi-Square Test

• Introduction to Deep Learning
• AI Vs ML Vs DL
• Neural Networks
• Forward and Backward Propagation
• Gradient descent, Stochastic gd , mini batch, Adadelta, adagrad, rmsprop
• Activation functions sigmoid,tanh,relu,leaky relu,softmax,p-relu,softplus 
• Adam optimizer , Sigmoid vanishing gradient problem 
• Advantages , disadvantages and applications 
• ANN implementation 
• CNN implementation

• Introduction of Generative AI
• Generative AI for Text Generation
• Generative AI for Machine Translation
• Generative AI for Creative Content Generation
• Advanced Topics in Generative AI with NLP
• LLMs: Use Cases and Potentials
• Hugging Face Hub: A Gateway to Generative AI
• Prompt Engineering: The Art of Guiding LLMs
• RAG: A Versatile Tool for Generative AI
• Fine-tuning: Unveiling the Potential of LLMs
• Project : On prompt image and caption generator
• Project : Customised Chat bot
• Project : Genei: An Alexa like assistant

• Need of AWS 
• Free tier account creation 
• Introduction to AWS home 
• Regions and availability zones 
• Billing dashboard
• Understanding EC2 
• Creating and launching ec2 instance
• Deployment of project on ec2
• Introduction to all available services provided by AWS (Theory) 
• AWS Sagemaker – Creating notebook instance 
• Building model using inbuilt jupyter sample notebook 
• Creating and deleting IAM role 
• AWS S3 – Introduction 
• Creating buckets and uploading objects into it 
• Integrating S3 with Sagemaker 
• Emptying and Deleting buckets