Advanced Full Stack Data Science With ML and AI

Master tools like Jupyter, Python IDLE, RStudio, Git, GitHub, and delve into concepts like Linear Regression, Logistic Regression, Neural Networks, Bag-of-Words Models, Machine Translation, Generative Adversarial Networks, and more.

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1000+

Students Trained

240+

Hours of Lectures

Google Ratings:

4.8

Duration

4 to 6 Months

Hybrid Mode

Online + Offline

Micro Batches

15 Students Only Batch Size

Eligibility

Anyone

Beginner Friendly

Beginner to Advanced Training

Course Offerings

Job Assistance


Daily Assignments and Exercises

Interview Preparations

Doubt Clearing


Resume Building


Mock Interviews


Study Material and Resources

Micro Batches


Regional Trainers


Beginner Friendly Curriculum

Internship Letter


Global Certifications


Course Curriculum

Introduction to Data Science:

Data science is a multidisciplinary field, blending statistics, mathematics, computer science, and domain expertise. It focuses on collecting, cleaning, processing, analyzing, and visualizing large datasets to make informed decisions and solve real-world problems.

Probability and Statistics:

Covering probability basics, laws, random variables, distributions, Central Limit Theorem, hypothesis testing, Bayesian Inference, Markov Chains, Monte Carlo Simulations, and their relevance in machine learning. Topics include descriptive statistics, probability distributions, inferential statistics, hypothesis testing, regression, correlation, experimental design, A/B testing, and time series analysis.

Python Programming:

Learn Python essentials, including setup with IDEs like IDLE and VSCode, working with local development environments and PyCharm. Topics cover numbers, arithmetic, printing, operators, strings, variables, input handling, conditionals, booleans, lists, loops (for and while), dictionaries, functions, split/join/slice operations, files, directories, paths, CSV data manipulation with Pandas, comprehensions, first-class functions, lambda expressions, imports, mapping, filtering, and code organization with iterators and generators.

Introduction to Python for Data Science:

Initiate your journey with data science by first installing and setting up Anaconda Python and Jupyter for a seamless environment. Dive into the fundamentals of NumPy, covering array creation, indexing, slicing, and essential functions, along with reshaping arrays. Continue with an introduction to Pandas, understanding data structures, DataFrame operations, data loading/saving, and essential manipulations. Explore the basics of Matplotlib and Seaborn for creating various plots and visualizations.

Analysis on a Dataset and Data Preprocessing:

Transition to practical applications with an analysis on a dataset, forming hypotheses and engaging in data preprocessing and wrangling. Learn key techniques for data collection, exploration, and handling missing values, outliers, and data cleaning. Delve into feature engineering, covering data transformation, splitting, scaling, integration, reduction, and aggregation. Understand additional techniques such as binning, encoding categorical data, handling text data through preprocessing, and managing date, time, and geospatial data for a comprehensive data science skill set.

Foundations of Machine Learning :

Introduction to Machine Learning.Supervised Learning and Unsupervised Learning,Linear Regression,Feature Engineering and Data Preparation,Logistic Regression,Logistic Regression Model,Binary Logistic Regression,Multi-Class Logistic Regression,KNN - K Nearest Neighbours,KNN for Classification,KNN for Regression,Support Vector Machines,SVM for Classification, Regression

Mastery and Real-world Applications :

Tree Based Methods,Decision Trees,Random Forests,Gradient Boosting,Random Forests,Random Forest Model,Random Forest for Classification & Regression,BoostingMethods,AdaBoost,Gradient Boosting,XGBoost (Extreme GradientBoosting),Naive Bayes Classification,Naive Bayes Variants,Gaussian Naive Bayes,Multinomial Naive Bayes,Bernoulli Naive Bayes,Apply different supervised machine learning techniques to find out the result on the based conditions from the preprocessed dataset K-means Clustering,K-means Model,Hierarchical Clustering,Hierarchical Clustering Model,DBSCAN - Density-based spatial clustering of applications with noise,DBSCAN Model,PCA - Principal Component Analysis and Manifold Techniques,PCA Model,Manifold Learning Techniques,Model Deployment - finally on the dataset apply the ML and make the model fit for the dataset.

Introduction to AI and Deep Learning Essentials :

Introduction to Artificial Intelligence,Introduction to Deep Learning - significance, applications, trends,Introduction to Neural Networks - neurons, layers, activation functions,Implementing basic activation functions and neuron operations.

Building Blocks of Neural Networks :

Basic Activation Functions,Neural Network Architecture,Weight Initialization Strategies,Forward Propagation,Backpropagation,Building a simple feedforward neural network with backpropagation. Implementing weight updates using gradient descent,Loss Functions,Regularization,Application in CNNs,Implementing different loss functions and optimization techniques. Adding regularization and dropout to a neural network.

Advanced Neural Network Techniques :

MLPs, activation functions, hyperparameter tuning, optimization techniques, implementing MLPs with TensorFlow/Keras, fine-tuning for optimal performance. Advanced MLPs, TensorFlow basics, CNNs, implementing CNN layers with TensorFlow/Keras, constructing CNN architecture, pooling layers, transfer learning, pre-trained models, and fine-tuning CNNs,Introduction to sequence models, importance, and applications of sequence data. Basics of sequence modeling, Hidden Markov Models (HMMs), implementing a simple Markov chain model for text prediction, creating a basic HMM for sequence generation.

Introduction to Recurrent Neural Networks (RNNs), implementing a basic RNN model with TensorFlow/PyTorch, creating an LSTM-based sequence generator, Gated Recurrent Units (GRUs). Building a sequence classifier using RNNs or GRUs and training an RNN/GRU for sentiment analysis.Self-attention and multi-head attention, exploring self-attention mechanisms, calculating attention scores. Multi-head attention and combining multiple attention heads. Advanced Transformers and BERT, introduction to BERT, pretraining, and fine-tuning of BERT, applications in NLP.

NLP, Computer Vision, and Cutting-edge Applications :

Uncover NLP essentials—text processing basics, preprocessing techniques, and Python libraries like NLTK and spaCy. Explore text representation, featuring Bag-of-Words, TF-IDF, and word embeddings, alongside distributed word representations. Move to sentiment analysis, introducing pipelines and supervised text classification with evaluation metrics.

Transition to sequence labeling, NER, POS tagging, and grammar parsing, incorporating HMMs and dependency parsing. Delve into text generation, language models, and challenges of N-gram models. Navigate through RNNs, LSTMs, and WSD with WordNet. Explore machine translation and NMT.

In the realm of Computer Vision, grasp image basics, manipulation with Python libraries, and ML-based image classification. Progress to keypoint detectors, descriptors, and CNN understanding. Learn CNN architecture, transfer learning, and fine-tuning for customized image classification.

Discover object detection methods, modern techniques, and segmentation with U-Net. Unveil Mask R-CNN for combined detection and segmentation. Explore GANs, comprehend their architecture, applications, and ethical considerations. Conclude with Advanced techniques—image super-resolution, neural style transfer, and insights into Deep Fakes' risks and ethical concerns.

Projects Building and Interview Preparation :

Building Multiple Complete Data Science Projects. Building a Machine Learning Project with Data Sets. Building Real world AI projects Mastering Group Discussions. Mastering Personal Interview Questions. Mock Interviews, Communication and Presentation Skills. Resume Building.

Build 40+ Projects from Scratch

Predictive Maintenance (PdM) using Machine Learning (ML)

Enhance efficiency with ML-driven proactive maintenance. Predict failures, optimize schedules, reduce downtime, and cut costs by analyzing historical data.

Document Analysis and Text Extraction

Automate document analysis and text extraction using ML and AI. Extract valuable insights, enhance data retrieval, and streamline document processing.

Fraud Detection System

Deploy an ML-based Fraud Detection System to safeguard against fraudulent activities. Analyze patterns and anomalies for swift detection and prevention.

Predictive Maintenance for IT Infrastructure with Data Science

Implement Predictive Maintenance for IT Infrastructure using ML and AI. Anticipate and prevent failures, optimize maintenance schedules, and enhance operational reliability.

AI-Driven Code Completion

Accelerate coding efficiency with an AI-driven Code Completion project. Leverage ML models for real-time suggestions, reducing manual effort and enhancing productivity.

Cybersecurity Threats Detector

Enhance cybersecurity defenses with a Threat Detector project using ML and AI. Analyze patterns, detect anomalies, and mitigate threats proactively.

Once you have completed the course, you will be able to generate your certificate and will also be eligible for placement assistance.

  1. Attendance of at least 80% of the classes.
  2. Completion of 80% of the projects and assignments assigned by the Company.

Note: Internship letter and global certifications will be issued exclusively upon the successful completion and submission of 80% of the projects and assignments.

Get Additional 2 Global Certifications

Clients Who Trust Us

Our Students and curriculum have been trusted by over 500+ companies across India

No Hidden Charges

Job Assistance Program

  • Placement opportunities until you get your job
  • Internship Letter after project completion (Add-on)
  • Online+Offline Classes - HYBRID
  • 40+ Projects, Daily Assignments and Exercises
  • Industry standard curriculum by experts and IIT graduates.
  • Live Classroom Instructor Led Classes. No Recorded Sessions
  • 1-1 live doubt support [Unlimited]
  • Dedicated relationship manager.
  • Dedicated, focused, personalised placement assistance.
  • Micro Batches 15-20 Students Only Batch Size
  • 2 Global Certifications
  • Study Materials, Resources, Handbook access and Mobile app access.

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