All certification tracks Bright Minds Certification track Python AI & Data Science Professional From zero to job-ready in 6 months 6 months 24 weeks

5 days/week

Live & Recorded Classes 01 6 Mo Duration 24 Weeks 14 Modules 5d/wk Live Classes A beginner-friendly 6-month track that takes you from Python basics through machine learning, deep learning, and Keras. Work with real datasets—from Loan Prediction to ensemble models—and leave with a portfolio recruiters actually open. Learn → Practice → Build → Deploy. Fourteen focused modules over 24 weeks—from Python basics through machine learning, deep learning, and Keras.

Outcome: internship and junior-role ready. What you will gain ✓Master Python for data analysis, from fundamentals to production libraries

✓Clean, explore, and model real-world datasets with Pandas and scikit-learn

✓Apply supervised, unsupervised, and ensemble ML techniques with confidence

✓Build and train neural networks with PyTorch and Keras

✓Portfolio with end-to-end projects—not notebook demos

✓Job-ready for intern and junior data/AI roles Built for →Complete beginners starting their data career

→Students and graduates targeting data analyst or ML roles

→Career switchers with no prior coding background

→Self-learners who want structure, projects, and accountability 01Curriculum Four phases. Zero fluff. 01 Python for Data Analysis — Foundations Module 1 Weeks 1–2 Why learn Python for data analysisInstalling Python and running simple programsLists, strings, tuples, and core data structuresIteration, conditional constructs, and control flow Outcome:Write foundational Python scripts—logic and data structures ready for analysis 02 Python Libraries & the Data Ecosystem Module 2 Weeks 3–4 NumPy, SciPy, Matplotlib, and PandasScikit-learn, Statsmodels, Seaborn, and BokehBlaze, Scrapy, SymPy, Requests, and LLM applications Outcome:Explore and compare key data-science libraries on sample datasets 03 Pandas — Series, DataFrames &

• EDA Module 3 Weeks 5–6 Introduction to Series and DataFramesExploratory analysis in Python using PandasData munging, distribution analysis, and quick exploration Outcome:End-to-end exploratory analysis on a real-world dataset 04 Building Predictive Models in Python Module 4 Weeks 7–8 Loan Prediction Problem — practice datasetImporting libraries and loading the datasetLogistic Regression, Decision Trees, and Random Forest Outcome:Train and compare classification models on the Loan Prediction dataset 05 Machine Learning &

• Data Science in Industry Module 5 Weeks 9–10 What is ML, AI, and Data Science — overview and motivationReal-world applications: predictive analytics, recommendations, process optimizationRole of ML in decision making across industriesJupyter Notebooks, Python IDEs, libraries, and career roadmap Outcome:Set up your learning workplace and map a personal ML career path 06 Programming Foundations for ML Module 6 Weeks 11–12 Python refresher: syntax, data types, control structures, functions, error handlingNumPy: array operations and linear algebra basicsPandas: DataFrames, manipulation, merging and joining datasetsVisualization: Matplotlib, Seaborn, and Plotly for EDA Outcome:Practical lab — clean, transform, and visualize a real-world dataset 07 Mathematics &

• Statistics for Machine Learning Module 7 Weeks 13–14 Linear algebra: vectors, matrices, eigenvalues and eigenvectorsCalculus: derivatives, gradients, and optimization basicsProbability & statistics: distributions, hypothesis testing, confidence intervalsBayesian thinking and how math underpins ML algorithms Outcome:Apply statistical and linear-algebra concepts to an ML problem 08 Data Acquisition, Cleaning &

• Preprocessing Module 8 Weeks 15–16 Data sourcing: SQL/NoSQL databases, APIs, and web scrapingHandling missing values, outlier detection, normalization, and scalingFeature engineering and encoding categorical variables Outcome:Acquire, clean, and preprocess a messy industry dataset end to end 09 Exploratory Data Analysis (EDA) Module 9 Week 17 Techniques to summarize and visualize data trends and patternsHands-on lab: end-to-end EDA on a sample industry dataset Outcome:Deliver a visual insight report with actionable findings from raw data 10 Supervised Learning Techniques Module 10 Weeks 18–19 Regression: linear, polynomial, Ridge, and Lasso — MSE and R² evaluationClassification: logistic regression, decision trees, k-NN, SVM, and Naive BayesModel evaluation: confusion matrices, precision, recall, F1-score, ROC curves Outcome:Build, train, and evaluate regression and classification models with scikit-learn 11 Unsupervised Learning &

• Anomaly Detection Module 11 Week 20 Clustering: K-Means, hierarchical clustering, DBSCAN, and elbow methodDimensionality reduction: PCA and t-SNEAnomaly detection: identifying outliers and unusual patterns Outcome:Apply clustering and dimensionality reduction to a real-world dataset 12 Neural Networks &

• Deep Learning Module 12 Weeks 21–22 Neural network fundamentals: layers, activation functions, backpropagationCNNs for image data

• RNNs and LSTM for sequential dataTransfer learning basics and introduction to PyTorch Outcome:Hands-on project — build and train a neural network for a classification task 13 Advanced Topics &

• Ensemble Methods Module 13 Week 23 Ensemble techniques: bagging, boosting (AdaBoost, Gradient Boosting, XGBoost), stackingReinforcement learning overview and Q-learning basicsHyperparameter tuning: cross-validation, grid search,



random searchModel interpretability with SHAP and LIME Outcome:Case study — improve model performance on a benchmark dataset using ensembles 14 Deep Learning with Keras Module 14 Week 24 Introduction to Keras and building a neural networkTraining and evaluating a model in KerasBackpropagation review and end-to-end deep learning workflow Outcome:Build, train, and evaluate a Keras model — capstone-ready deep learning project 02Interview Ready What companies actually test Master the skills screened in every backend hiring loop—product startups, mid-size companies, and service firms. Python &

• Data Manipulation Critical Pandas, NumPy, data cleaning, feature engineering, data munging, file I/O Machine Learning Critical Regression, classification, clustering, ensemble methods, scikit-learn, model evaluation Deep Learning Critical Neural networks, CNNs, RNNs/LSTM, PyTorch, Keras, transfer learning Statistics &

• Probability High Distributions, hypothesis testing, linear algebra, Bayesian thinking Data Visualization High Matplotlib, Seaborn, Plotly, EDA reporting, data storytelling Model Optimization High Hyperparameter tuning, cross-validation, SHAP, LIME, XGBoost 03Placement Real Project Experience from Month 4 After 3 months of structured learning, you shift to industry-style work—real datasets, team collaboration, and end-to-end AI solutions. Month 4 Industry Project Kickoff Assigned real datasets, team roles, and project scoping—simulate actual workflow Month 5 Build &

• Iterate End-to-end AI solution development with mentor reviews and peer collaboration Month 6 Capstone &

• Demo Final project presentation, viva, and portfolio polish for job applications Ongoing Portfolio &

• Placement Course certificate, project portfolio, and internship-ready profile What you walk away with ✓Loan Prediction classification model with model comparison report

✓End-to-end EDA and predictive modeling capstone on an industry dataset

✓Neural network project built with PyTorch or Keras

✓Ensemble model case study with interpretability analysis (SHAP/LIME)

✓GitHub portfolio recruiters can evaluate in minutes 04Certification BlueFox Certified AI & Data Science Professional Awarded on completion of all modules, continuous assessments, and a capstone project demo—proof you can build, not just watch.

Requirements →Pass weekly coding tests and module assignments

→Complete all 14 modules with mini projects submitted

→Deliver capstone project with live demo and viva

→Portfolio with at least 2 end-to-end AI/data projects Lifetime credential with verifiable digital certificate 05Class Format How you'll learn Live Classes +5 days/week

2–3 hours daily (live sessions + guided practice)

+Weekly rhythm: 3 days learning

1 day practice

1 day project/review

+Continuous assessment—weekly tests, assignments, and mini projects Recorded Classes +All live sessions recorded with chapter markers

+On-demand lab walkthroughs for Pandas, scikit-learn, PyTorch, and Keras modules

+Replay complex topics at your own pace between live classes Taught by working data scientists and AI engineers building production models daily. 06Career Roles you'll be ready for Data AnalystML EngineerAI EngineerData ScientistBusiness Intelligence Analyst Ready to apply? Pick this track, share your details, and we'll reply within one business day. [email protected] Placement Support After 3 months of structured learning, you shift to industry-style work—real datasets, team collaboration, and end-to-end AI solutions.

Starts

Week 20 3 months post-program support Other certification tracks Complete Backend Developer React Full Stack Developer Master Class Software Testing &

• Quality Assurance Master Class 01Curriculum Four phases. Zero fluff. 01 Python for Data Analysis — Foundations Module 1 Weeks 1–2 Why learn Python for data analysisInstalling Python and running simple programsLists, strings, tuples, and core data structuresIteration, conditional constructs, and control flow Outcome:Write foundational Python scripts—logic and data structures ready for analysis 02 Python Libraries & the Data Ecosystem Module 2 Weeks 3–4 NumPy, SciPy, Matplotlib, and PandasScikit-learn, Statsmodels, Seaborn, and BokehBlaze, Scrapy, SymPy, Requests, and LLM applications Outcome:Explore and compare key data-science libraries on sample datasets 03 Pandas — Series, DataFrames &

• EDA Module 3 Weeks 5–6 Introduction to Series and DataFramesExploratory analysis in Python using PandasData munging, distribution analysis, and quick exploration Outcome:End-to-end exploratory analysis on a real-world dataset 04 Building Predictive Models in Python Module 4 Weeks 7–8 Loan Prediction Problem — practice datasetImporting libraries and loading the datasetLogistic Regression, Decision Trees, and Random Forest Outcome:Train and compare classification models on the Loan Prediction dataset 05 Machine Learning &

• Data Science in Industry Module 5 Weeks 9–10 What is ML, AI, and Data Science — overview and motivationReal-world applications: predictive analytics, recommendations, process optimizationRole of ML in decision making across industriesJupyter Notebooks, Python IDEs, libraries, and career roadmap Outcome:Set up your learning environment and map a personal ML career path 06 Programming Foundations for ML Module 6 Weeks 11–12 Python refresher: syntax, data types, control structures, functions, error handlingNumPy:



array operations and linear algebra basicsPandas: DataFrames, manipulation, merging and joining datasetsVisualization: Matplotlib, Seaborn, and Plotly for EDA Outcome:Practical lab — clean, transform, and visualize a real-world dataset 07 Mathematics &

• Statistics for Machine Learning Module 7 Weeks 13–14 Linear algebra: vectors, matrices, eigenvalues and eigenvectorsCalculus: derivatives, gradients, and optimization basicsProbability & statistics: distributions, hypothesis testing, confidence intervalsBayesian thinking and how math underpins ML algorithms Outcome:Apply statistical and linear-algebra concepts to an ML problem 08 Data Acquisition, Cleaning &

• Preprocessing Module 8 Weeks 15–16 Data sourcing: SQL/NoSQL databases, APIs, and web scrapingHandling missing values, outlier detection, normalization, and scalingFeature engineering and encoding categorical variables Outcome:Acquire, clean, and preprocess a messy industry dataset end to end 09 Exploratory Data Analysis (EDA) Module 9 Week 17 Techniques to summarize and visualize data trends and patternsHands-on lab: end-to-end EDA on a sample industry dataset Outcome:Deliver a visual insight report with actionable findings from raw data 10 Supervised Learning Techniques Module 10 Weeks 18–19 Regression: linear, polynomial, Ridge, and Lasso — MSE and R² evaluationClassification: logistic regression, decision trees, k-NN, SVM, and Naive BayesModel evaluation: confusion matrices, precision, recall, F1-score, ROC curves Outcome:Build, train, and evaluate regression and classification models with scikit-learn 11 Unsupervised Learning &

• Anomaly Detection Module 11 Week 20 Clustering: K-Means, hierarchical clustering, DBSCAN, and elbow methodDimensionality reduction: PCA and t-SNEAnomaly detection: identifying outliers and unusual patterns Outcome:Apply clustering and dimensionality reduction to a real-world dataset 12 Neural Networks &

• Deep Learning Module 12 Weeks 21–22 Neural network fundamentals: layers, activation functions, backpropagationCNNs for image data

• RNNs and LSTM for sequential dataTransfer learning basics and introduction to PyTorch Outcome:Hands-on project — build and train a neural network for a classification task 13 Advanced Topics &

• Ensemble Methods Module 13 Week 23 Ensemble techniques: bagging, boosting (AdaBoost, Gradient Boosting, XGBoost), stackingReinforcement learning overview and Q-learning basicsHyperparameter tuning: cross-validation, grid search, random searchModel interpretability with SHAP and LIME Outcome:Case study — improve model performance on a benchmark dataset using ensembles 14 Deep Learning with Keras Module 14 Week 24 Introduction to Keras and building a neural networkTraining and evaluating a model in KerasBackpropagation review and end-to-end deep learning workflow Outcome:Build, train, and evaluate a Keras model — capstone-ready deep learning project 02Interview Ready What companies actually test Master the skills screened in every backend hiring loop—product startups, mid-size companies, and service firms. Python &

• Data Manipulation Critical Pandas, NumPy, data cleaning, feature engineering, data munging, file I/O Machine Learning Critical Regression, classification, clustering, ensemble methods, scikit-learn, model evaluation Deep Learning Critical Neural networks, CNNs, RNNs/LSTM, PyTorch, Keras, transfer learning Statistics &

• Probability High Distributions, hypothesis testing, linear algebra, Bayesian thinking Data Visualization High Matplotlib, Seaborn, Plotly, EDA reporting, data storytelling Model Optimization High Hyperparameter tuning, cross-validation, SHAP, LIME, XGBoost 03Placement Real Project Experience from Month 4 After 3 months of structured learning, you shift to industry-style work—real datasets, team collaboration, and end-to-end AI solutions. Month 4 Industry Project Kickoff Assigned real datasets, team roles, and project scoping—simulate actual workflow Month 5 Build &

• Iterate End-to-end AI solution development with mentor reviews and peer collaboration Month 6 Capstone &

• Demo Final project presentation, viva, and portfolio polish for job applications Ongoing Portfolio &

• Placement Course certificate, project portfolio, and internship-ready profile What you walk away with ✓Loan Prediction classification model with model comparison report

✓End-to-end EDA and predictive modeling capstone on an industry dataset

✓Neural network project built with PyTorch or Keras

✓Ensemble model case study with interpretability analysis (SHAP/LIME)

✓GitHub portfolio recruiters can evaluate in minutes 04Certification BlueFox Certified AI & Data Science Professional Awarded on completion of all modules, continuous assessments, and a capstone project demo—proof you can build, not just watch.

Requirements →Pass weekly coding tests and module assignments

→Complete all 14 modules with mini projects submitted

→Deliver capstone project with live demo and viva

→Portfolio with at least 2 end-to-end AI/data projects Lifetime credential with verifiable digital certificate 05Class Format How you'll learn Live Classes +5 days/week

2–3 hours daily (live sessions + guided practice)

+Weekly rhythm: 3 days learning

1 day practice

1 day project/review

+Continuous assessment—weekly tests, assignments, and mini projects Recorded Classes +All live sessions recorded with chapter markers

+On-demand lab walkthroughs for Pandas, scikit-learn, PyTorch, and Keras modules

+Replay complex topics at your own pace between live classes Taught by working data scientists and AI engineers building production models daily. 06Career Roles you'll be ready for Data AnalystML EngineerAI EngineerData ScientistBusiness Intelligence Analyst

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