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Data Science and Artificial Intelligence (AI) Training with Internship Program

  • Data Science and Artificial Intelligence (AI) Training with Internship Program

Data Science and Artificial Intelligence (AI) Training with Internship Program

  • Welcome to S-Logix, where we offer comprehensive and hands-on training in data science to equip aspiring professionals with the skills and knowledge needed to excel in this rapidly growing field. Our training program is designed to provide a solid foundation in data science concepts, tools, and techniques, empowering our students to tackle real-world challenges and make informed data-driven decisions.

Why Choose Us?

  • Experienced and Expert Faculty: Learn from industry-experienced instructors who are passionate about data science and dedicated to your success.
  • Practical Approach: Our curriculum emphasizes hands-on learning and practical projects to ensure you gain practical skills and experience.
  • Comprehensive Curriculum: Our comprehensive curriculum covers the fundamental concepts of data science, including data preprocessing, machine learning, deep learning, and data visualization.
  • Industry-Relevant Skills: Gain the in-demand skills and knowledge needed to thrive in the data science job market, with a focus on the latest tools and technologies.
  • Real-World Projects: Apply your skills to real-world projects and case studies, working with actual datasets to solve complex problems.
    • Career Support: Receive career guidance, interview preparation, and job placement assistance to help you launch your data science career.
    • Flexible Learning Options: Choose from full-time or part-time training options, allowing you to tailor your learning experience to your schedule and goals.
    • State-of-the-Art Infrastructure: Learn in a conducive and technology-driven environment with access to the latest software, tools, and resources.
    • Course Duration: Four Months + Two Months Internship
    • Full Time: 4 Hrs (Monday – Friday) - Offline 2 Hrs Theory Session + 2hrs Practical Session

  • Students Looking for a Professional Career in DS and AI: B. E / B.Tech / M.E / M.Tech / M.Sc / Masters with any discipline
  • Aspiring Data Scientists: Individuals looking to start a career in data science and gain the necessary skills to analyze data and extract valuable insights.
  • Professionals Seeking Career Transition: Professionals from diverse backgrounds who want to transition into the field of data science and enhance their career prospects.
  • Business Analysts: Business professionals who want to develop data-driven decision-making skills and leverage data to drive business growth.
  • IT Professionals: IT professionals interested in expanding their skill set to include data science and machine learning techniques.
  • Python Installation and Set up the Working Environment
  • Fundamentals of Python
  • List and Boolean variables
  • Sets, Dictionaries & Tuples
  • Functional, Positional and Keyword Arguments
  • Functions and Modules
  • OOPS concepts
  • List Comprehension
  • Dictionary comprehension
  • String Formatting
  • File Handling
  • Exception Handling
  • Data Structures
  • Generators and Iterators
  • Regular Expression
  • Numpy
  • Pandas
  • Seaborn
  • Matplotlib
  • Sklearn
  • Data Acquisition (Import & Export)
  • Indexing
  • Selection and Filtering Sorting & Summarizing
  • Combining and Merging Data Frames
  • Removing Duplicates
  • Discretization and Binning
  • Data and Data Types
  • Measures of Central Tendency in Data
  • Measures of Dispersion
  • Understanding Skewness in Data
  • Data Distribution
  • Sampling and Sampling Distributions
  • Hypothesis Testing - Large and Small Samples
  • Analysis of Variance and Covariance
  • Exploratory Data Analysis
  • Feature Engineering
  • Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Information Gain
  • Classification
    • Logistic Regression
    • Decision Tree
    • Random Forest
    • Neural Networks
    • SVM
    • KNN
    • Naive Bayes
  • Regression
    • Linear Regression
    • Random Forest Regressor
  • Clustering
    • K-Mean clustering
    • Hierarchical Clustering
  • Association Rule Mining
  • Semi-Supervised Learning
  • Ensemble Learning
  • Data Visualization & Reporting
  • Machine Learning Model testing, validation and performance Evaluation
    • Confusion Matrix
    • Precision
    • Recall
    • F-Measure
    • Accuracy
    • AUC
    • MSE
    • Performance Visualization
  • Introduction to Artificial Intelligence and Neural Networks
  • History and Evolution of Deep Learning
  • Deep Learning Frameworks and Tools
  • Setting up the Development Environment
  • Basics of Neural Networks
  • Activation Functions and Loss Functions
  • Feed Forward/Backpropagation Algorithm
  • Optimization Techniques (Gradient Descent, Adam, RMSProp)
  • Introduction to CNN
  • Convolutional Layers, Pooling Layers, and Padding
  • Object Detection and Image Classification using CNN
  • Transfer Learning and Fine-tuning CNN Models
  • Introduction to RNN
  • Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
  • Language Modeling and Text Generation using RNN
  • Sequence-to-Sequence Models and Machine Translation
  • Introduction to GANs
  • Generator and Discriminator Networks
  • Training GANs and Mode Collapse
  • Applications of GANs (Image Generation, Style Transfer)
  • Introduction to Reinforcement Learning
  • Markov Decision Processes (MDP) and Q-Learning
  • Deep Q-Network (DQN) and Policy Gradient Methods
  • Applications of Deep Reinforcement Learning (Game Playing, Robotics)
  • Autoencoders and Variational Autoencoders
  • Attention Mechanisms in Deep Learning
  • Transformer Models for Natural Language Processing
  • Deep Learning for Time Series Analysis and Forecasting
  • Model Regularization and Dropout
  • Hyperparameter Tuning and Cross-Validation
  • Model Evaluation Metrics and Techniques
  • Model Deployment and Serving (TensorFlow Serving, Flask API)
  • Introduction to NLP and its Applications
  • Basics of Text Processing and Linguistics
  • Language Models and Corpora
  • NLP Tools and Libraries (NLTK, SpaCy)
  • Text Preprocessing and Tokenization
  • Text Representation and Feature Extraction
  • Text Cleaning and Normalization
  • Tokenization and Lemmatization
  • Stop Word Removal and Stemming
  • Part-of-Speech Tagging
  • Bag-of-Words Model
  • TF-IDF Vectorization
  • Word Embeddings (Word2Vec, GloVe)
  • Document Similarity and Vectorization Techniques
  • Sentiment Analysis Techniques
  • Lexicon-based Approaches
  • Machine Learning Models for Sentiment Analysis
  • Aspect-based Sentiment Analysis
  • Named Entity Recognition and Entity Extraction
  • Introduction to Named Entity Recognition (NER)
  • NER Techniques and Algorithms
  • Rule-based and Machine Learning-based NER
  • Entity Extraction and Relation Extraction
  • Supervised Text Classification
  • Introduction to Topic Modeling (LDA)
  • Latent Semantic Analysis and Latent Dirichlet Allocation
  • Sequence-to-Sequence Models and Machine Translation
  • Introduction to Machine Translation
  • Encoder-Decoder Models
  • Attention Mechanisms for NLP
  • Neural Machine Translation
  • Natural Language Generation and Text Summarization
  • Introduction to Natural Language Generation (NLG)
  • Text Generation Techniques (Markov Chains, RNNs)
  • Text Summarization Techniques (Extractive, Abstractive)
  • Multimodal Deep Learning
  • Federated Deep Learning
  • Self-Supervised Learning
  • Zero Short and Few Short Learning
  • Computer Vision Tasks
  • Pretrained Image Models
  • Real Time Object Detection
  • Image to Text
  • Text to Image

Real-World Projects: Machine Learning

  • Project 1: Supervised Learning (Regression)
  • Project 2: Supervised Learning (Classification)
  • Project 3: Unsupervised Learning (Clustering)
  • Project 4: Application-Sentiment Analysis and Opinion Mining
  • Project 5: Application-Recommender Systems

Real-World Projects: Deep Learning

  • Project 1: MLP
  • Project 2: RNN
  • Project 3: CNN
  • Project 4: Advanced Deep Learning-Based Projects1
  • Project 5: Advanced Deep Learning-Based Projects2

Project Internships

  • Candidates who completed the course successfully are eligible.
  • Duration: Two Months – Real-time projects based on client requirements.
  • Stipend: Successful candidates are eligible for a stipend.