PYTHON INTRODUCTION
Lecture:1 What is Python?
Lecture:2 Python History
Lecture:3 Python 2.x vs 3.x
Lecture:4 Features of python
Lecture:5 About Python Versions
Lecture:6 Applications of python
INSTALLATION OF PYTHON
Lecture:7 How to install Python
Lecture:8 Python Script mode
Lecture:9 Python GUI mode
Lecture:10 Python Interactive Mode
Lecture:11 Python In Linux
Lecture:12 How to execute a python script
Lecture:13 Windows GUI mode
Lecture:14 How to Install ANACONDA
Lecture:15 How to set Path
BASICS OF PYTHON
Lecture:16 Python “Hello World”
Lecture:17 How to Execute Python
Lecture:18 Variables in python
Lecture:19 Keywords in python
Lecture:20 Identifiers in python
Lecture:21 Literals in python
Lecture:22 Operators in python
Lecture:23 Comments in python
PYTHON STRINGS
Lecture:24 Accessing Strings
Lecture:25 Strings Operators
Lecture:26 Basic Operators
Lecture:27 Membership Operators
Lecture:28 Relational Operators
Lecture:29 Slice Notation
Lecture:30 String Functions and Methods
PYTHON LISTS
Lecture:31 How to define list
Lecture:32 Accessing list
Lecture:33 Elements in a Lists
Lecture:34 List Operations
Lecture:35 Adding Lists
Lecture:36 Replicating lists
Lecture:37 List slicing
Lecture:38 Updating elements in a List
Lecture:39 Appending elements to a List
Lecture:40 Deleting Elements from a List
Lecture:41 Functions and Methods of Lists
PYTHON TUPLES
Lecture:42 How to define tuple
Lecture:43 Accessing tuple
Lecture:44 Elements in a tuple
Lecture:45 Tuple Operations
Lecture:46 Tuple slicing
Lecture:47 Deleting tuple
Lecture:48 Functions and Methods of tuple
PYTHON DICTIONARY
Lecture:49 How to define dictionary
Lecture:50 Accessing Dictionary
Lecture:51 Updating
Lecture:52 Deletion
Lecture:53 Functions and Methods
PYTHON CONTROL STATEMENTS
Lecture:54 “If” in python
Lecture:55 “If else” in python
Lecture:56 “else if” in python
Lecture:57 “nested if” in python
Lecture:58 “for loop” in python
Lecture:59 “while loop” in python
Lecture:60 “break” in python
Lecture:61 “continue” in python
Lecture:62 “pass” in python
PYTHON FUNCTIONS
Lecture:63 Defining a Function
Lecture:64 Invoking a Function
Lecture:65 return Statement
Lecture:66 Argument and Parameter
Lecture:67 Passing Parameters
Lecture:68 Default Arguments
Lecture:69 Keyword Arguments
Lecture:70 Anonymous Function
Lecture:71 Difference between Normal Functions and Anonymous Function
Lecture:72 Scope of Variable
PYTHON I/O
Lecture:73 “print” statement
Lecture:74 Input from Keyboard
FILE HANDLING
Lecture:75 Operations on Files
Lecture:76 Opening file
Lecture:77 closing file
Lecture:78 reading file
Lecture:79 writing file
Lecture:80 Modes of files
Lecture:81 Methods in files
PYTHON OOPS CONCEPT
Lecture:82 Python OOPs Concepts
Lecture:83 Python Object Class
Lecture:84 Python Constructors
Lecture:85 Python Inheritance
Lecture:86 Multilevel Inheritance
Lecture:87 Multiple Inheritance
Lecture:88 Function Overriding
PYTHON MODULES
Lecture:88 Importing a Module
Lecture:89 Example of importing multiple modules
Lecture:90 How to use “from” import statement
Lecture:91 import whole module
Lecture:92 Built in Modules in Python
Lecture:93 Package
PYTHON EXCEPTIONS
Lecture:94 What is Exception handling
Lecture:95 Declaring Multiple Exception
Lecture:96 Finally Block
Lecture:97 Raise an Exception
Lecture:98 Custom Exception
PYTHON DATE AND TIME
Lecture:99 Retrieve Time
Lecture:100 Formatted Time
Lecture:101 time module
Lecture:102 date module
Lecture:103 date time module
ADVANCED CONCEPTS OF PYTHON
Lecture:104 Math module
Lecture:105 Random module
Lecture:106 Sys module
Lecture:107 List Comprehension
Lecture:108 Closure
Lecture:109 Iterator
Lecture:110 Generator
Lecture:111 Decorators
Lecture:112 Map function
Lecture:113 Filtering
Lecture:114 Zipping
Lecture:115 with Statement
Lecture:116 Keyword arguments
Lecture:117 *args vs. ** kwargs
Lecture:118 Code Introspection
Lecture:119 Regular Expressions
DATA VISUALISATION USING MATPLOTLIB AND SEABORN
Introduction to data visualisation
Chart properties
Chart styling
Heat Maps
Box plots
Scatter plots
Bubble charts
3D charts
Time Series
Graph data
Geographical data
Importing datasets and libraries
Color Palette
Histogram
Visualize pairwise relationship
Statistical Estimation
Kernel Density Estimation
Facet Grid
Pair Grid
Visualizing Statistical Relationships
Introduction to Probability and Statistics
Population,Sample, Types of Variables
Descriptive Statistics
The measure of central tendency, the measure of spread, five points summary
Correlation, covariance,Inter Quartile Range(IQR)
Probability Distributions, Probability in Data Analytics
Probability Distributions, Binomial distribution, Poisson distribution, Bayes Theorem, central limit theorem
Inferential Statistics
Confidence intervals,F-test, Z-test, t-test, ANOVA, chi-square test, etc.
Hypothesis Testing,Type-I error,Type-II error
Realtime Business Problem Case Studies on
Sales, Marketing & Retail Domain
1.Analyzing Customer Purchase Patterns Using Descriptive Statistics in Retail.
2.Evaluating the Effect of Advertising Spend on Sales: A Correlation and Regression Study.
3.Identifying Seasonality in Online Sales Using Time Series Analysis.
4.Customer Segmentation Using Cluster Analysis on Sales Data.
5.Measuring Impact of Price Changes on Product Demand Using Elasticity Models.
Banking & Finance Domain
6.Risk Analysis of Loan Defaults Using Probability Distributions.
7.Testing the Efficiency of a Portfolio Using Statistical Tools.
8.Time Series Forecasting of Stock Prices Using Moving Averages.
9.Comparing Returns of Mutual Funds Using ANOVA.
10.Regression Analysis of Economic Indicators and Market Trends.
Manufacturing & Quality Control Domain
11.Statistical Process Control in a Manufacturing Line: A Six Sigma Case Study.
12. Reducing Product Defects Using Hypothesis Testing in Quality Assurance.
13. Applying Pareto Analysis to Identify Key Causes of Downtime in Production.
14. Measuring Process Variability Using Standard Deviation and Control Charts.
15. Evaluating Supplier Performance Using Statistical Sampling.
HR & Business Operations Domain
16. Using Statistics to Analyze Employee Attrition Rates.
17. Hypothesis Testing to Assess the Impact of Training on Productivity.
18. Forecasting Workforce Needs Using Regression and Time Series.
19. Exploring Gender Pay Gap Through Inferential Statistics.
20. Employee Satisfaction Survey Analysis Using Measures of Central Tendency.
E-commerce & Digital Domain
21. Web Traffic Analysis Using Descriptive and Inferential Statistics.
22. A/B Testing to Evaluate Website Design Impact on Conversion Rate.
23. Correlation Between Delivery Time and Customer Satisfaction in E-Commerce.
Healthcare & Medical Research Domain
24. Analyzing Patient Waiting Time Using Measures of Central Tendency.
25. Survival Analysis of Patients Undergoing Different Treatment Protocols.
26. Correlation Between BMI and Blood Pressure in Adults.
27. Hypothesis Testing to Evaluate New Drug Effectiveness.
28. Predicting Hospital Readmission Using Logistic Regression.
Introduction to Machine Learning
1.Supervised, Unsupervised learning.
2.Introduction to scikit-learn, Keras, etc.
Regression
3.Introduction classification problems, Identification of a regression problem, dependent and independent variables
4.How to train the model in a regression problem?
5.How to evaluate the model for a regression problem?
6.How to optimize the efficiency of the regression model?
Classification
1.Introduction to classification problems, Identification of a classification problem, dependent and independent variables
2.How to train the model in a classification problem?
3.How to evaluate the model for a classification problem?
4.How to optimize the efficiency of the classification model?
Clustering
1.Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables
2.How to train the model in a clustering problem?
3.How to evaluate the model for a clustering problem?
4.How to optimize the efficiency of the clustering model?
Supervised Learning
1.Linear Regression-Creating linear regression models for linear data using statistical tests.
2.Logistic Regression-Creating logistic regression models for classification problems.
3.Support Vector Machine(SVM)
4.Naive Bayes Classifier
5.K-Nearest Neighbour(KNN)
6.Decision Tree
7.Random Forest Classifier
Ensemble Learning
1.Introduction to Baggging and Boosting Algorithms
2.AdaBoost (Adaptive Boosting) Algorithm
3.Gradient Boosting Machines (GBM) Algorithm
4.XGBoost (Extreme Gradient Boosting) Algorithm
5.LightGBM (Light Gradient Boosting Machine) Algorithm
6.Stochastic Gradient Boosting Algorithm
7.LPBoost (Linear Programming Boosting) Algorithm
Unsupervised Learning
1.K-means-The K-means an algorithm that can be used for clustering problems in an unsupervised learning approach
2.Dimensionality reduction-Handling multi dimensional data and standardizing the features for easier computation
3.Linear Discriminant Analysis-LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data
4.Principal Component Analysis-PCA follows the same approach in handling multidimensional data
Performance Metrics
1.Classification reports-To evaluate the model on various metrics like recall, precision, f-support, etc.
2.Confusion matrix-To evaluate the true positive/negative, and false positive/negative outcomes in the model.
r2, adjusted r2, mean squared error, etc.
3.Time Series Forecasting-Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting
Time Series Forecasting
1.Resampling, Autocorrelation, Forecasting, Seasonal
2.Naive, Double/Triple Exponential (Holt) Residual Analysis, Stationarity tests
3.Autoregressive methods, moving averages, ARIMA, SARIMA
[ARTIFICIAL INTELLIGENCE (AI)]
What is AI?
Types of AI.
Advantages of AI.
Applications of AI in Current Era.
What is Reinforcement Learning?
Types of RL.
Advantages of Reinforcement Learning.
Applications of Reinforcement Learning.
Models used for Reinforcement Learning.
Key features of RL.
Elements of RL.
What is Agent?
What is Environment?
What is reward and Punishment in RL.
Bellman Equation.
Reinforcement Learning Models.
Markov Decision Process.
What is Q-Learning?
Application of RL Models.
[Natural Language Processing (NLP)]
What is NLP?
Advantages of NLP.
Applications of NLP in Practical Fields.
Types of NLP.
Various Tools/Libraries used for NLP.
Installing NLP Packages.
Working with NLTK Package.
Rule based NLP vs. Statistical NLP.
Tokenizing words and sentences with nltk.
Word Level Analysis.
Syntactic Analysis.
Semantic Analysis.
Part of Speech Tagging(PoS)
Stemming, Lemmatization
Chunking, Chinking
Named Entity Recognition (NER).
WordNet
Bag of Words.
Practical Project Implementation of NLP.
[COMPUTER VISION]
What is Computer Vision?
Need of Computer Vision?
Packages used for Computer Vision.
Introduction to Computer Vision
Introduction to OpenCV
Installing OpenCV
Storing Images
Reading Images
Writing Images
Image Conversion
Colored Image to Binary
Grayscale to Binary
Drawing Functions
Drawing a Circle
Drawing a Line
Drawing a Rectangle
Drawing an Ellipse
Drawing Polylines
Adding Text
Blur Techniques
Filtering
Bilateral Filter
Box Filter
SQRBox Filter
Filter2D
Dilation
Erosion
Morphological Operations
Image Pyramids
Thresholding
Simple Threshold
Adaptive Threshold
Adding Borders
Transformation Operations
Laplacian Transformation
Distance Transformation
Camera and Face Detection
Face Detection in a Picture
Face Detection using Live Camera
Geometric Transformation
Affine Translation
Rotation
Scaling
Color Maps
Canny edge Detection
Practical Project Implementation using Open CV.
[DEEP LEARNING]
Introduction to Deep Learning.
Advantages of Deep Learning.
Applications of Deep Learning.
Packages used for Deep Learning.
Models used for Deep Learning.
What is Neural Networks?
Types of Artificial Neural Networks.
Convolutional Neural Networks(CNN)
Application of CNN.
Intuition behind CNN.
Practical Implementation of CNN.
Recurrent Neural Network (RNN).
Application of RNN.
Intuition behind RNN.
Practical Implementation of RNN.
LSTM (Long Short Term Memory).
Application of LSTM.
Multilayer Perceptron.
Application of Multilayer Perceptron.
Generative Adversarial Networks (GANs).
Restricted Boltzman Machine (RBM).
Deep Belief Network.
Auto Encoder.
Application of Deep Learning Models.
[DEEP LEARNING WITH KERAS]
Introduction to Keras.
Installing Keras.
Keras Layers.
Keras Models
Keras Model Compilation
Regression Prediction using MPL.
Time Series Prediction using LSTM and RNN.
Keras Vs. Tensorflow vs.PyTorch Vs.CNTK Vs.Theano
Project Implementations using Keras.
[DEEP LEARNING WITH TENSORFLOW]
Introduction to Tensorflow.
Installing Tensorflow.
CPU vs. GPU vs. TPU.
Tensorflow Basics: Tensors, Shapes, Types, Sessions, Operators.
What is Tensor board?
What is Placeholder?
Data frame and Data range use of pandas.
How to import CSV Datasets in TF?
Linear regression using TF.
Tensorflow Optimizers.
Gradient Decent Optimization.
Convolutional Neural Network using TF.
Recurrent Neural Networks using TF.
CNN vs. RNN
Multilayer Perceptron using TF.
Hidden Layer Perceptron.
Practical Project Implementations of Tensorflow.
What is Google Colab?
How to run Tensor Flow on Google Colab.
1. Introduction to Vertex AI
Overview of Google Cloud AI Platform, components, and key use cases.
2. Data Preparation & Storage
BigQuery, Cloud Storage integration, and feature engineering using Vertex AI Feature Store.
3. Model Training
Custom model training, AutoML, and distributed training using prebuilt containers or custom code.
4. Model Deployment
Model registry, endpoint creation, and real-time or batch prediction.
5. Pipelines & MLOps
Building end-to-end ML pipelines using Vertex AI Pipelines and orchestration with Kubeflow.
6. Hyperparameter Tuning
Automated hyperparameter search using Vertex AI Vizier.
7. Monitoring & Model Management
Continuous evaluation, drift detection, and model versioning.
9. Responsible AI
Bias detection, explainable AI (XAI), and model fairness tools.
10. Hands-on Labs & Real-World Projects
Build, deploy, and monitor an ML model using Vertex AI on GCP.
MLOps Introduction
What is MLOps?
MLOps Lifecycle
MLOps Capabilities
Getting Started with MLOps
MLOps - ML Development
MLOps - Model Building and Training
MLOps - Training Operationalisation
MLOps - Model Versioning
MLOps - Model Registry
MLOps - Model Governance
MLOps - Model Deployment
MLOps - Prediction Serving
MLOps - Model Monitoring
StreamLit
What is Streamlit?
Why should data scientists use Streamlit?
How to use Streamlit
Install Streamlit
How to run your Streamlit code
Display texts with Streamlit
Display an image, video or audio file with Streamlit
Input widgets
Display progress and status with Streamlit
Sidebar and container
Display graphs with Streamlit
Display maps with Streamlit
Themes
Build a machine learning application
How to deploy a Streamlit app
Generative AI
1. Foundations of Generative AI
a. Machine Learning (ML) Paradigms
b. Neural Networks, Architectures, Activation Functions, Optimization Techniques
c. Representation Learning, Embeddings, Feature Engineering
d. Probabilistic Models, Bayesian Networks, Hidden Markov Models (HMMs)
e. Reasoning and Planning
f. Natural Language Processing, Tokenization, Part-of-Speech (POS) tagging,
Named Entity Recognition (NER), Word2Vec
g. Computer Vision, Image classification, Object detection, Image segmentation
h. Foundation Models and Their Roles
2. Language Modeling and Transformers
a. Sequential Data Modeling, Recurrent Neural Networks (RNNs),
Encoder-Decoder Models
b. Natural Language Generation and Understanding
c. Multilingual Language Models, Cross-lingual learning
d. Attention Mechanisms, Transformers, Self-attention, Multi-head attention
e. Pre-trained Transformers: BERT, GPT, T5, XLNet, LaMDA, etc.
f. Conditional Language Generation, Text summarization, Question answering
g. Textual Encoding and Decoding: Tokenization, Byte Pair Encoding (BPE)
3. Large Language Models
a. Weight, Bias and Parameters of Language Models
b. Reasoning and Commonsense Knowledge Integration
c. Multimodal Learning and Embeddings
d. Memory and Efficiency Optimization
e. GPT, LLaMA, LaMDA, PaLM, Gemini, Falcon, BLOOM
f. Zero-shot and Few-shot Learning
g. Evaluation Metrics for LLMs
4. Generative AI and LLM Frameworks
a. Tensorflow and PyTorch
b. Hugging Face
c. Lang Chain
d. Llama Index
e. Generative AI providers - OpenAI, Cohere, Anthropic, LLMFlow
f. Generative AI Agents, AutoGPT, AgentGPT, BabyAGI
g. Code Generative Tools - Amazon CodeWhisper, OpenAI Codex
h. Open-source Tools and Resources for Generative AI
5. Image Generative Models
a. Autoencoder and its Variants
b. Generative Adversarial Networks (GANs)
c. Style Transfer and Image Transformation
d. Latent Diffusion Model
e. Stable Diffusion
f. DALL.E
g. Contrastive Language-Image Pre-Training (CLIP)
h. Attention Mechanisms in Image Generation
i. Hierarchical Text-Conditional Image Generation with CLIP Latents
6. Prompt Engineering
a. Prompt Design Strategies
b. Task Formulation in Prompts
c. Prompt Patterns
d. Prompt Tuning Techniques
e. Fine-tuning Prompts for Specific Tasks
f. Domain-specific Prompt Engineering
g. Dynamic and Adaptive Prompting
h. Zero-shot learning, Chain-of-thought, Self-consistency
i. Evaluating Prompt Performance
7. Vector Databases and Search
a. Vector Databases
b. High-dimensional Data Storage
c. Vector Embeddings
d. High-Dimensional Semantic Similarity
e. Personalized Search, Multimodal Search, Knowledge Graph Search
f. Semantic search, Conversational search, Visual search
g. Personalization and Relevance Ranking
h. Evaluation of Search Systems
8. Fine Tuning and Optimizing the LLMs
a. Hyperparameter Tuning and Optimization
b. Data Augmentation for Fine-tuning
c. Prompt Tuning
d. Retrieval-Augmented Generation (RAG)
e. Parameter Efficient Fine Tuning (PEFT) Techniques
f. Reinforcement Learning from Human Feedback (RLHF)
g. Efficient Training Pipeline
9. Deployment and Scaling of Generative Models
a. Model Deployment Strategies
b. Scalability and Resource Management for Generative Models
c. Automated Pipelines for Model Deployment
d. Versioning and Rollback Strategies
e. Model Monitoring and Performance Tracking
f. Interoperability and Compatibility
g. Robustness and Error Handling in Model Deployment
h. Security Measures in Model Deployment
i. AIOps and LLMOps