Python Data Science Development

Module 1: Introduction to Artificial Intelligence and Machine Learning

• Artificial Intelligence

• What is Machine Learning?

• Machine Learning algorithms

• Supervised Versus Unsupervised Learning

• Machine Learning Algorithms

  • Regression

  • Classification

  • Clustering

• Applications of Machine Learning

• Machine learning examples

• Setting up Anaconda & Python Notebooks.

• Working on notebooks for Data Science

Module 2: Techniques of Machine Learning

• Supervised learning

• Unsupervised learning

Module 3: Mathematics & Statistics Refresher

• Concepts of linear algebra

• Euclidean and Non-Euclidean geometry

• Introduction to Calculus

• Probability, Conditional Probability, Bayes Theorem

• Distributions, CDF, PDF

• Mean, Median, Mode

• Variance & Correlation,

• Standard Deviation, quartiles, percentiles

• Variable Relationships & Estimation

• Hypothesis Testing

Module 4: Accessing/Importing and Exporting Data

• Importing Data from various sources (Csv, txt, excel…etc)

• Viewing Data objects

• Exporting Data to various formats

• Important python modules: numpy, pandas, scipy etc.

Module 5: Introduction to NumPy, Pandas

• Create arrays using NumPy

• Perform various operations on arrays and manipulate them

• Indexing slicing and iterating

• Read & write data from text/CSV files into arrays and vice-versa

• Create Series and Data Frames in Pandas

• Data structures & index operations in pandas

• Importing and exporting data

• Indexing and slicing of data structures in pandas

• Reading and Writing data from Excel/CSV formats into Pandas

Module 6: Data Cleaning- Manipulation

• Basic Functionalities of a data object

• Merging of Data objects

• Concatenation of data objects

• Types of Joins on data objects

• Exploring a Dataset

• Analysing a dataset

• Data Manipulation steps (sorting, filtering, duplicates, merging, appending, derived variables, sampling, Data type, conversions, renaming, formatting etc)

• Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc)

• Python Built-in Functions (Text, numeric, date, utility functions)

• Normalizing data

• Formatting data

Module 7: Data Analysts-Visualization

• Introduction exploratory data analysis

• Descriptive statistics, Frequency Tables and summarization

• Univariate Analysis (Distribution of data & Graphical Analysis)

• Bivariate Analysis (Distributions & Relationships, Graphical Analysis}

• Creating Graphs-

  • Bar plot

  • Pie plot

  • Count plot

  • Line chart

  • Histogram

  • Boxplot

  • Scatter

  • Density

  • Violine Plot

  • Swarm plot

  • Distplot

  • Pair plot

  • Heatmap

• Important Packages for Data Visualisations

  • Matplotllb

  • Seaborn

  • Plotly

  • Cufflinks

SUPERVISED LEARNING:

Module 8: Linear Regression

• Introduction- Applications

• Assumptions of Linear Regression

• Building Linear Regression Model

• Understanding standard metrics

• (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)

• Assess the overall effectiveness of the model

• Validation of Models

• Interpretation of Results- Business Validation

• Implementation on new data

Module 9: Logistic Regression

• Introduction- Applications

• Linear Regression Vs. Logistic Regression

• Building Logistic Regression Model

• Understanding standard model metrics

• Confusion matrix, accuracy score

• Standard Business Outputs

• Interpretation of Results- Business Validation

• Implementation on new data

Module 10: Time Series Forecasting

• Introduction -Applications

• Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition

• Classification of Techniques

• Basic Techniques- Averages, Smoothening, etc

• Advanced Techniques- AR Models, ARIMA, etc

Module 11: Decision Trees

• Decision Trees - Introduction-Applications

• Construction of Decision Trees through Simplified Examples

• Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical variables; other

• Measures of Randomness

• Pruning a Decision Tree; Cost as a consideration

• Decision Trees – Validation

• Over fitting- Best Practices to avoid

Module 12: Ensemble Learning

• Concept of Ensembling

• Manual Ensembling Vs. Automated Ensembling

• Methods of Ensembling (Stacking, Mixture of Experts)

• Bagging (Logic, Practical Applications)

• Random forest (Logic, Practical Applications)

• Boosting (Logic, Practical Applications)

• Ada Boost

• Gradient Boosting Machines (GBM)

• XGBoost

Module 13: Naive Bayes

• Concept of Conditional Probability

• Bayes Theorem and Its Applications

• Naive Bayes for classification

• Applications of Naive Bayes in Classifications

Module 14: Model Evaluation, Improvements & Performance Metrics

• Data Split Practices

• Cross Validation

• K-Fold Validation

• Confusion Matrix

• ROC Curves

• Mean Absolute/Square Errors & R-Square

• Ensemble Learning & Model Stacking

Module 15: Kernel Learning

• Support Vector Machines

• Principal Component Analysis

• Ridge Regression

• Spectral Clustering

Module 16: Support Vector Machines

• Motivation for Support Vector Machine &Applications

• Support Vector Regression

• Support vector classifier (Linear &Non-Linear)

• Interpretation of Outputs and Fine tune the models with hyper parameters

• Validating SVM models

Module 17: Unsupervised Learning: Segmentation

• What is segmentation & Role of ML in Segmentation?

• Clustering algorithms

• Concept of Distance and related math background

• K-Means Clustering, Elbow method

• Hierarchical Clustering

Module 18: Natural Language Processing

• What is NLP & How to solve NLP problems

• NLP Feature Engineering & Modelling

• How to process any raw data file

  • Tokenizing, remove stopwords, speech tagging

  • Stemming, Lemmatizing, CountVectorizer, Wordcloud

• Build models for solving practical read world problems.

Module 19: Deep Learning - Artificial Neural Networks (ANN)

• Motivation for Neural Networks and Its Applications

• Perceptron and Single Layer Neural Network, and Hand Calculations

• Learning In a Multi Layered Neural Net: Back-Propagation and Conjugant Gradient Techniques

• Introducing & Using Tensorflow

• Neural Networks for Regression

• Neural Networks for Classification

• Interpretation of Outputs and Fine tune the models with hyper parameters

• Validating ANN models

Module 20: End to End ML Implementation and Use Case specific discussions