AI / ML / DataScience Course

Foundations + Tools + Data Analytics

• Python programming -basics .
• Descriptive, Predictive & Prescriptive analytics .
• Variable types.
• Measures of central tendency & dispersion.
• Key visualization charts and tables .
• Sampling.
• Central limit theorem .
• Law of large numbers.
• Laws of probability and their applications.
• Hypothesis testing.
• 4 key statistical distributions.
  • Normal distribution.
  • T -distribution
  • Chi Square Distribution
  • F – Distribution
• Estimation
  • P-value.
  • Confidence interval
  • Test of significance
• Introducing Linear Regression.
  • Correlation.
  • Concept of best fit line.
    • R squared and Adjusted R squared.
  • Outliers and their effects
  • Regression vs Causation.
  • Error Metrics.-RMSE,MAPE,

Foundations + Tools + Machine Learning 1

• Statistical learning vs Machine learning.
• Models and modelling.
• Linear Algebra basics.
  • Principal Component Analysis (PCA).
• Vectors and Calculus.
• Gradient descent vs Ordinary Least Square Method.
• Python programming for Machine learning -Key libraries.
  • Pandas
  • Numpy
  • Scikit learn
  • Matplotlib
  • Seaborn
  • Scipy
• Data Cleaning
  • Null value and Outlier identification and imputation techniques.
• Data Transformations.
  • Common transformation techniques for categorical variables.
  • Feature scaling techniques.
  • PCA
• Data visualization using python libraries
• Solving real life problems through linearity and non linearity cases.
  • Multiple Linear Regression
  • Ridge Regression
  • Lasso regression
  • Elastic net
• Feature Engineering :
  • Curse of dimensionality
  • Variable selection strategy
  • One hot encoding for categorical variables
  • Label encoding for categorical variables
• Bias -Variance trade offs :
  • Overfitting vs Underfitting
  • Regression Errors and diagnostics
• Parametric vs Non parametric models :
  • Explain ability vs Black box
  • Industry applications
• Data split techniques
  • Cross validation vs train_test splits .
• Improve model accuracy through Hyper-parameter tuning
  • Grid search.
  • Random search .

Foundations + Tools + Machine Learning 2

• ·SQL basics
  • Data types & Operators
  • Maths
  • Tables
  • Data extraction and Transformations
  • Strings
• Classification vs Regression
  • Basic concepts
  • Used cases and industry applications
• 7 -Classification models
• Decision boundary and errors .
• Logistic Regression
  • Difference wrt Linear regression.
  • Concept of Sigmoid function.
  • Intuitive and Mathematical understanding
  • Hyper pameter tuning.
  • Used cases and Industry application.
• Support vector Machines
  • Concept of hyper plane
  • Intuitive and mathematical understanding
  • Hyper parameter tuning.
  • Used cases and Industry applications
• Naïve Bayes
  • Concept of Naïve bayes theorm .
• Intuitive and mathematical understanding.
• Used cases and Industry applications.
• K NN
  • Concept of Distance metrics.
  • Intuitive and mathematical understanding.
  • Hyper parameter tuning.
  • Used cases and industry applications.
• Decision trees.
  • Concept of Entropy and Information gain.
  • Intuitive and mathematical understanding.
  • Hyper parameter tuning.
  • Used cases and industry applications.
• Ensembling.
  • Concept of ensemble.
  • Intuitive and mathematical understanding.
  • Used cases and industry applications.
• Ensemble -Bagging and Boosting.
  • Concept of bootstap.
  • Key differences between bagging and boosting.
• Random forest -Bagging
  • Intuitive and mathematical understanding.
  • Hyper parameter tuning.
  • Used cases and industry applications.
• Ada boost -Boosting.
  • Intuitive and mathematical understanding.
  • Hyper parameter tuning.
  • Used cases and industry applications.
• Classification Evaluation metrics.
  • Classification accuracy.
  • Confusion metrics.
  • ROC curve.
  • Classification score -FI score.
• Model Selection strategy.
• Machine learning pipelines.
• Unsupervised learning.
  • Key differences between Supervised and unsupervised learning.
  • Concept of clustering.
  • Clustering with K Means.
  • Intuitive and mathematical understanding.
  • Used cases and industry applications.
• Clustering with Dendrograms.
  • Intuitive and graphical display.
  • Used cases and industry applications.

Foundations + Tools + Deep Learning

• Calculus-Partial Differentiation.
• Python for deep learning.
  • Python libraries –
    • Tensor flow
    • Keras
• Deep learning -Used cases and Industry applications.
• Multi layer perceptions.
• Key Activation functions.
• Deep and wide architectures.
• Training networks.
• Backward propagation.
• Hyper parameters.
• Regularization using drop outs.
• Artificial Intelligence.
  • Structured and Unstructured data.
  • NLP basics.
  • Computer vision basics.

Tools + Data Visualization + Business Intelligence

• Business story telling basics.
  • Data , Narratives & Infographics .
  • Importance of narratives backed by key infographics.
  • Understanding 2 compelling styles.
  • Gestalt principles of visual perception.
• Business story telling using Tableau.
  • Dimensions & Measures.
  • Marks card .
  • Filters .
  • Key analytics .
  • Groups & Sets.
  • Parameters .
  • Fields .
  • Joints.
  • Regex operations.
  • LOD (Level of details) .
  • Conditional statements .
  • Key charts and tables.
  • Geographical Maps , Pareto Charts, Word cloud.

• 20 Industry case studies.
• 10 Class assignments. 
• 5 Module exams.
• 1 Certification Exam