Data Science, ML, Deep Learning & GEN AI with Python Learn from basic of Data Science to Advance GEN AI
In this course we will learn Different ML Algo's, Image Processing, NLP & Generative AI from basics..
This is perfect course for Beginners or Professionals who want to learn Complete AI from basic to advance.
Learning Mode
Offline Classes
Projects
10+
Duration
6 Months
Data Science, Machine Learning, Deep Learning and GEN. AI with Python.
From image processing with OpenCV to mastering CNN and YOLO models , we cover it allIn this comprehensive course, you will delve into the foundations of data science and machine learning, unraveling the mysteries behind algorithms and their applications.
you'll be learning both supervised and unsupervised learning algorithms, from linear regression and decision trees to clustering.
Ever wondered how computers "see" objects? With our course, you'll explore the realm of convolutional neural networks (CNN), the revolutionary technology behind image classification.
But that's not all –We will work on GEN AI models, how ChatGPT works, and How to generate images from Text.
Unleash your creativity and apply Transfer Learning techniques to customize the YOLO model for your specific needs.
Upcoming Batches
Weekdays
Offline
Batch Start Date:Thu Dec 12 2024
Batch Time:
1. 4:30 PM
Fee:
11,000
Apply Now
What you'll learn
This curriculum designed by Industry expert for you to become a next industry expert. Here you will not only learn, you will implement your learnings in real time projects.
Basic Python and It's Fundamental Concept
1 Month
Discover the magic of Python by effortlessly installing it and setting up your development environment.
After Installing Python we will be mastering the following fundamental Concepts.
Hello! World Program
Data Types
Variables
Array
Objects
Control Flow
Loops
Functions and Modules
Functions and Modules
File handling
Libraries
For Each topics, we will do hands on practice by solving many programs which will help you to build your logics and Increase your Imagination Power.
Data Science - Working with Data
1 Month
Explores data manipulation and analysis using Python. Focuses on NumPy for numerical computing, including creating and manipulating arrays. Introduces Pandas for data manipulation, cleaning, preprocessing, and exploratory data analysis.
Working with Data
Introduction to data manipulation and analysis.
Creating and manipulating arrays - NumPy
Introduction to Pandas for data manipulation
Data structures (Series, DataFrame)
Data cleaning and preprocessing techniques.
Data Visualization - MatplotLib & Plotly
Introduction to data visualization and its importance
Matplotlib for 2D plotting - Line plots, scatter plots, bar plots, histograms and more.
Categorical plots, distribution plots, regression plots with Seaborn.
Plotly for interactive and web-based visualizations.
Creating interactive plots and dashboards
Image Processing with Python & OpenCV
1 Weeks
Introduces image processing and computer vision concepts. Students learn to work with the OpenCV library in Python, including image reading, writing, manipulation, and filtering.
Introduction to image processing and computer vision.
Installing and using the OpenCV library in Python
Introduction to Pandas for data manipulation
Image reading, writing, and manipulation
Image filtering techniques (blurring, sharpening, thresholding)
Feature extraction techniques (edge detection, corner detection)
Object detection using Haar cascades
After this section we will create face detection project, where we will trainig our machine with multiple faced, and detect the correct face.
Play with Machine Learning
3 Weeks
Provides an introduction to machine learning and focuses on supervised learning algorithms. Students learn to build and evaluate models for regression and classification tasks.
Lern different algorithms of supervised learning.
Introduction to machine learning and supervised learning.
Simple linear regression, multiple linear regression
Model evaluation metrics (mean squared error, R-squared)
Logistic regression for binary classification.
Gradient descent optimization algorithm
Naive Bayes for probabilistic classification
Linear SVM, kernel trick, nonlinear classification.
K-Nearest Neighbors (KNN) algorithm.
Decision Trees for classification and regression
Explores unsupervised learning algorithms, specifically clustering. Covers K-means Clustering and Hierarchical Clustering. Students learn to group data based on similarities and evaluate clustering performance.
Fun with un-supervised learning.
Clustering algorithms overview
K-means Clustering for grouping data
Elbow method for cluster evaluation.
Hierarchical Clustering for creating tree-like clusters.
Deep Learning - TensorFlow and Keras
2 Weeks
Introduces the fundamentals of deep learning. Covers artificial neural networks, backpropagation algorithm, and building multi-layer networks with Keras. Includes topics like activation functions, weight initialization, and model saving/restoring.
Deep Learning Fundamentals
Introduction to artificial neural networks (ANN)
Perceptron and activation functions
Feedforward neural networks and forward propagation
Multi-layer networks with Keras
Activation functions, weight initialization, regularization
Saving and restoring a trained model for future use
Advanced Deep Learning
Introduction to Convolutional Neural Networks (CNN)
CNN architectures: AlexNet, VGGNet, InceptionNet, ResNet
Convolutional layers, pooling layers, fully connected layers.
Introduction to Generative Adversarial Networks (GAN)
Training GANs for image generation and manipulation
Transfer Learning and Image Classification
Introduction to transfer learning
Using pre-trained models and transfer learning with Keras
Working with the ImageNet dataset
Building image classification projects using transfer learning
Fine-tuning pre-trained models
Machine Learning is Exciting: Inspiring Projects
3 Weeks
1. Movie Recommendation
2. Stock Price Prediction
3. Iris Flower Classification
4. Digit Recognition
5. Handwritten Digit Generation
Apart from these basic app, we will create 3 advanced projecs.,
Fruit Image Classifier.
Mask Detection of Face
Gender Recognition
Image Style Transfer : Color the image
"Your ideas matter to us! We welcome captivating project suggestions to enhance our training.
Let's collaborate on creating an engaging and impactful learning experience that brings your ideas to life and develops valuable skills.
Natural Language Processing (NLP)
1 Month
Introduction to NLP
Definition and importance of NLP
Overview of how NLP enables computers to understand and process human language
Applications of NLP in the real world (e.g., chatbots, sentiment analysis, language translation)
Exercise: Discuss the potential use cases of NLP in various industries
Text Preprocessing
Definition and importance of NLP
Word tokenization vs. sentence tokenization
Tools for tokenization (e.g., nltk, spaCy)
Stop Words Removal
Definition and significance of stop words
Common methods for removing stop words using nltk and spaCy
Stemming and Lemmatization
Differences between stemming and lemmatization
Popular algorithms (Porter Stemmer, Snowball Stemmer, and lemmatization using WordNetLemmatizer)
Text Normalization
Lowercasing, punctuation removal, and whitespace handling
Exercise: Implement basic text preprocessing steps on a given paragraph
Feature Extraction from Text
Bag of Words (BoW) Model
Explanation of the BoW approach
Creating a BoW representation using CountVectorizer
Term Frequency-Inverse Document Frequency (TF-IDF)
Concept of term frequency and inverse document frequency
Implementing TF-IDF using TfidfVectorizer
Word Embeddings
Introduction to word embeddings and their importance in representing context
Overview of Word2Vec and GloVe
Exercise: Create a BoW and TF-IDF representation for sample text
Sentiment Analysis
Introduction to Sentiment Analysis
Explanation and applications of sentiment analysis
Tools and libraries for sentiment analysis (e.g., TextBlob, VADER)
Implementing Basic Sentiment Analysis
Using TextBlob for simple sentiment scoring
Using VADER for more nuanced sentiment analysis
Exercise:Perform sentiment analysis on sample tweets or product reviews
Text Classification
Preparing Data for Text Classification
Overview of labeled datasets for training
Converting text data into features for model training
Building a Text Classifier
Using Naive Bayes or Logistic Regression for text classification
Classifying news articles or spam detection
Evaluating Text Classification Models
Metrics for evaluation: Precision, Recall, F1-score
Exercise: Build a simple text classifier to identify spam messages
Named Entity Recognition (NER)
Introduction to NER
Understanding entities (e.g., names, dates, locations)
Practical use cases of NER (e.g., data extraction from documents)
Implementing NER
Using spaCy or nltk for NER
Customizing NER models for specific applications
Exercise:Extract named entities from a news article
Topic Modeling
Concept of Topic Modeling
Explanation of how topic modeling helps discover topics in a collection of documents
Applications in summarizing and organizing large datasets
Latent Dirichlet Allocation (LDA)
Basic understanding and implementation of LDA for topic discovery
Exercise: Implement LDA to find topics in a set of documents
Project: Sentiment Analysis
Performs sentiment analysis on user-provided text and visualizes the results.
Generative AI
1 Month
Introduction to Generative AI
What is Generative AI?
What are GANs?
How GANs Work
Exercise: Implement a simple GAN using a dataset of your choice (e.g., images or text).
Variational Autoencoders (VAEs)
What are VAEs?
How VAEs Work
Encoder
Decoder
Exercise: Build a VAE using a simple dataset like MNIST to generate new images.
Text Generation with Language Models
What is Text Generation?
How Text Generation Works
GPT-3 and its Applications
Exercise: Use a pre-trained GPT model to generate text based on a prompt of your choice.
Practical Applications of Generative AI
Image Generation
Using models like GANs or VAEs to generate new images.
generating artwork, creating realistic images from text descriptions
Text Generation
Text generation models for writing assistance, content creation, and chatbots.
virtual assistants and conversational AI.
Music and Audio Generation
Generating music using models trained on existing music data.
Sound design, and content creation.
Exercise: Explore a tool or library for generating AI-based images or text (e.g., DALL-E for images or GPT-2 for text generation).
Ethics and Challenges in Generative AI
Ethical Considerations:
How generative AI can be misused to create deepfakes or fake news.
Concerns around copyright and ownership of AI-generated content.
Challenges:
Difficulty in training GANs (e.g., mode collapse).
Balancing creativity and coherence in generated content.
Capstone Project: Generative AI-Based Image and Text Generator
1 Month
Objective
Build a pipeline to generate images and text:
Image Generation: Train a GAN to create images (e.g., MNIST, CIFAR-10).
Text Generation: Use GPT-2/GPT-3 to generate text based on prompts.
Steps
Preprocess Data: Collect and preprocess image and text datasets.
Image Generation with GAN: Train and evaluate a GAN.
Combine Image and Text: TCreate a pipeline for generating both from prompts.
Tools: Python: TensorFlow/PyTorch (GANs), Hugging Face (GPT-2/GPT-3).
Image Processing: OpenCV, PIL.
Technologies You Will Master Hands-On
During this program you will learn some most demanding technologies. We will develop some real time projects with the help of these technologies.
YoLo
OpenCV
Pandas
Scikit Learn
Numpy
Keras
Tensorflow
AWS
Matplotlib
Python
Program Fees
11,000
(incl. taxes)
If you will join in a group, complete group will get discount.
You can pay your fee in easy installment's. For more details you can connect with our team.
Meet Your Instructors
You will learn with industry expertes.
Prateek Mishra
CTO & Sr. TrainerEx -
Nikita Choudhary
Director - TrainingEx -
Atul Malhotra
Sr. Training Manager
What You Could Become
Transform Into a Full Stack Data Scientist - Your Path to Limitless Possibilities!
And many more...