Python – Programming Syllabus
The Syllabus of will be framed as per the requirement of corporate. We are mainly concentrating in the following.
Introduction to Python
⦁ Overview of Python- Starting with Python
⦁ Introduction to installation of Python
⦁ Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
⦁ Understand Jupyter notebook & Customize Settings
⦁ Concept of Packages/Libraries – Important packages(NumPy,SciPy,scikit-learn,Pandas,Matplotlib,etc)
⦁ Installing & loading Packages & Name Spaces
⦁ Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
⦁ List and Dictionary Comprehensions
⦁ Variable & Value Labels – Date & Time Values
⦁ Basic Operations – Mathematical – string – date
⦁ Reading and writing data
⦁ Simple plotting
⦁ Control flow & conditional statements
⦁ Debugging & Code profiling
Scientific distributions used in python for Data Science
⦁ Numpy, scify, pandas, scikitlearn etc
Accessing/Importing and Exporting Data using python modules
⦁ Importing Data from various sources (Csv, txt, excel, access etc)
⦁ Database Input (Connecting to database)
⦁ Viewing Data objects – subsetting, methods
⦁ Exporting Data to various formats
⦁ Important python modules: Pandas
Data Manipulation – cleansing – Munging using Python modules
⦁ Cleansing Data with Python
⦁ Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, 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)
⦁ Python User Defined Functions
⦁ Stripping out extraneous information
⦁ Normalizing data
⦁ Formatting data
⦁ Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
Data Analysis – Visualization using Python
⦁ Introduction exploratory data analysis
⦁ Descriptive statistics, Frequency Tables and summarization
⦁ Univariate Analysis (Distribution of data & Graphical Analysis)
⦁ Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
⦁ Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
⦁ Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)
Basic statistics & implementation of stats methods in Python
⦁ Basic Statistics – Measures of Central Tendencies and Variance
⦁ Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
⦁ Inferential Statistics -Sampling – Concept of Hypothesis Testing
⦁ Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square
⦁ Important modules for statistical methods: Numpy, Scipy, Pandas

Machine Learning
Predictive Modeling – Basics
⦁ Introduction to Machine Learning & Predictive Modeling
⦁ Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs.
Forecasting
⦁ Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
⦁ Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
Machine Learning Algorithms & Applications – Implementation in Python
⦁ Linear Regression
⦁ Segmentation – Cluster Analysis (K-Means)
⦁ Decision Trees (CART/CD 5.0)
⦁ Support Vector Machines(SVM)
⦁ Other Techniques (KNN, Naïve Bayes, )
⦁ Important python modules for Machine Learning (SciKit Learn, scipy, etc)
Deep Learning
⦁ Artificial Neural Networks(ANN)
Deep Learning Course Syllabus
Convolutional Neural Networks
⦁ Invariance, stability.
⦁ Variability models (deformation model, stochastic model).
⦁ Scattering networks
⦁ Group Formalism
⦁ Supervised Learning: classification.
⦁ Properties of CNN representations: invertibility, stability, invariance.
⦁ covariance/invariance: capsules and related models.
⦁ Connections with other models: dictionary learning, LISTA.
⦁ Other tasks: localization, regression.
⦁ Embeddings (DrLim), inverse problems
⦁ Extensions to non-euclidean domains
⦁ Dynamical systems: RNNs.
⦁ Guest Lecture
Deep Unsupervised Learning
⦁ Autoencoders (standard, denoising, contractive, etc etc)
⦁ Variational Autoencoders
⦁ Adversarial Generative Networks
⦁ Maximum Entropy Distributions

Miscellaneous Topics
⦁ Non-convex optimization for deep networks
⦁ Stochastic Optimization
⦁ Attention and Memory Models
⦁ Open Problems
Artificial Intelligence Course Syllabus
Artificial Intelligence is a changing technology and hence the syllabus is updated such that we deliver the latest and best. Algorithms and data are the key to artificial intelligence and we teach you the importance of them.
⦁ Artificial intelligence fundamentals
⦁ Computational mathematics for learning and data analysis
⦁ Machine learning
⦁ Human language technologies
⦁ Parallel and distributed systems: paradigms and models
⦁ Intelligent Systems for pattern recognition
⦁ Algorithm engineering (KD)
⦁ Data mining (KD)
⦁ Mobile and cyber-physical systems (ICT)
⦁ Real-time Data Warehouse migration
⦁ Information retrieval (KD)
⦁ Social and ethical issues in computer technology
⦁ Computational neuroscience (ING)
⦁ Robotics
⦁ Semantic web