Data science module 1: Power BI

Data Modeling, Data Visualization, DAX (Data Analysis Expressions), Power Query, Dashboard Design, Report Sharing and Collaboration, Data Connectivity and Preparation, Security and Administration, Power BI Service, Mobile Reporting

Data science module 2: Python and statistics

Python programming, data manipulation with pandas, data visualization with matplotlib and seaborn, probability theory, hypothesis testing, statistical distributions, linear regression, machine learning basics, inferential statistics, exploratory data analysis

Data science module 3: Big data analytics

Data Mining, Machine Learning, Predictive Analytics, Data Visualization, Natural Language Processing, Cloud Computing, Internet of Things (IoT), Real-time Analytics, Data Governance, Edge Computing

Data science module 4: Machine learning and deep learning

Supervised learning, Unsupervised learning, Neural networks, Convolutional neural networks (CNNs), Recurrent neural networks (RNNs), Natural language processing (NLP), Reinforcement learning, Generative AI, Feature engineering, Transfer learning

  1. Data analysis on canadian immigration project using pandas, numpy, matplotlib, and seaborn


  2. San francisco crime dataset visualization using folium framework


  3. NLTK library and wordcloud library for natural language processing and building data visualization


  4. Regression plots, bar plots, pair plots, facetgrids, boxplots, violin plots using seaborn data visualization on titanic dataset


  5. Data cleaning, outlier removal, normalization on automobile efficiency prediction dataset

  1. Applying one sample hypothesis tests - binomial test, wilcoxon rank test, student T-test and chi square test using scipy, researchpy, statsmodels and sklearn librarys on student statistics and general stat survey datasets


  2. Applying two sample paired and unpaired tests for finding if phenomenon is significant in the sample


  3. Finding which feature is statistically significant and will be retained for prediction


  4. Integrating gpt-3.5 and statsmodels to explain in simple language the results of statistical tests


  1. Applying linear regression and logistic regression on iris flower recognition and mnist handwritten digit recognition


  2. Implement naive bayes and tree models - random forest, boosting, bagging, and ensemble methods


  3. Calibrating classifiers, using pipelines, grid search and random search for hyper-parameter tuning.


  4. Calculating precision, recall, f1-score, confusion matrix and roc-auc.


  5. Projects on satellite image classification, food and traffic signal classification


  1. Applying artificial neural networks for image and text classification and regression


  2. Bias-variance tuning, optimizing networks and regularization


  3. Data augmentation for variance reduction. scrapping tweets from twitter.


  4. Deployment of deep learning app on cloud


  5. Case studies on skin melanoma, audio command recognition, text to image generation and text classification


  6. Project on seed classification, imdb movie sentiment analysis


  1. Postgresql for basic, intermidiate and complex queries for extracting data from world atlas, hotel management and attendance management databases


  2. GPT-3.5 api to build websites and deploy on the streamlit cloud through github ci/cd pipeline


  3. Dashboard building, power bi report and features


parisha bhatia
One of the Best place for Upskilling in Borivali, with Best Faculties giving their best to each of their Student.

- Parisha Bhatia

The best place to upgrade your software skills amidst experienced group of professionals.

- Heetansh Jhaveri

Utkarsh minds classes is best experience classes

Farhan Sayyed

I highly recommend Dr. Pranav Nerurkar and Utkarsh Minds Software Training Center. Dr. Pranav is a great professor and his patience and dedication to make every topic clear is unparalleled. He is very approachable and always works hard to help us.

Arth Akhouri

Tier-I engineer

20000(120 hours)

  • Introduction to AI:
    • Definition of AI
    • Brief history of AI
    • Applications of AI in various industries
    • Ethical considerations and societal impacts of AI
  • Fundamentals of Machine Learning:
    • Introduction to machine learning
    • Types of machine learning: supervised, unsupervised, reinforcement learning
    • Basic algorithms: linear regression, logistic regression, k-nearest neighbors
    • Evaluation metrics for model performance
  • Basic Tools and Programming:
    • Introduction to Python for AI
    • Libraries like NumPy, Pandas, and Matplotlib
    • Data preprocessing and manipulation
  • Problem Solving with AI:
    • Approaches to AI problem-solving
    • Search algorithms: breadth-first search, depth-first search
    • Basic game theory and decision trees
  • Essential Mathematics:
    • Linear algebra
    • Calculus
    • Probability and statistics basics
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Tier-II engineer

20000(120 hours)

  • Applied Machine Learning:
    • Advanced supervised learning algorithms: Support Vector Machines, Decision Trees, Random Forests
    • Unsupervised learning and clustering techniques: k-means, hierarchical clustering, PCA
    • Introduction to deep learning and neural networks
  • Data Handling and Visualization:
    • Advanced data processing and feature engineering
    • Data visualization techniques for better insights
  • Natural Language Processing (NLP):
    • Text preprocessing and tokenization
    • Word embeddings and vector space models
    • Introduction to NLP frameworks like NLTK, spaCy
  • Computer Vision:
    • Image processing fundamentals
    • Introduction to OpenCV
    • Basic neural network architectures for image classification
  • Reinforcement Learning:
    • Understanding the reinforcement learning framework
    • Markov decision processes
    • Basic RL algorithms like Q-learning and SARSA
  • Hands-on Labs and Projects:
    • Building and managing a application
    • Implementing a full-fledged pipeline with advanced workflows
    • Deploying and managing
  • Soft Skills:
    • Collaboration and Communication Skills
    • Project Management
    • Problem-Solving in Complex Systems
  • Additional Components:
    • Periodic Assessments and Quizzes
    • Real-world Case Studies and Scenarios
    • Best Practices and Industry Standards
    • Guest Lectures from Industry Experts
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Tier-III engineer

40000(240 hours)

  • Advanced Deep Learning:
    • Deep learning frameworks (TensorFlow, PyTorch)
    • Convolutional Neural Networks (CNNs) for image tasks
    • Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs) for sequence data
    • Generative models: GANs and Variational Autoencoders (VAEs)
  • AI Scaling and Optimization:
    • Distributed computing for AI
    • GPU acceleration and optimization techniques
    • Model compression and efficient neural networks
  • Advanced NLP and Sequences:
    • Attention mechanisms and transformer models
    • Pre-trained models: BERT, GPT
    • Sequence-to-sequence models for translation and summarization
  • Robotics and Control:
    • Introduction to robotics and autonomous systems
    • Probabilistic robotics and sensor integration
    • Path planning and obstacle avoidance
  • AI Strategy and Policy:
    • AI project management
    • Discussing AI policies, privacy, and regulatory considerations
    • Future trends and the direction of AI research
  • Hands-on Labs and Projects:
    • Building and managing a application
    • Implementing a full-fledged pipeline with advanced workflows
    • Deploying and managing
  • Soft Skills:
    • Collaboration and Communication Skills
    • Project Management
    • Problem-Solving in Complex Systems
  • Additional Components:
    • Periodic Assessments and Quizzes
    • Real-world Case Studies and Scenarios
    • Best Practices and Industry Standards
    • Guest Lectures from Industry Experts
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Our location is 2 minutes walk from Borivali railway station east

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