What you'll Learn

• Module1:- Introduction to Python- Core Python Concept, Python Character Set.
• Module2:- Basics of Python(Variables, Data Types, Operators, Input and Output, Expressions)
• Module3:- Working on conditional and Iterative statement.
• Module4:- Working on if, if-else, Nested if-else.
• Module5:- Implementing looping- For, While, Nested Loop.
• Module6:- Working on control statement- Break, Continue.
• Module7:- Sting Manipulation, Accessing, Basic Operations, String Slices.
• Module8:- Implementing Functions.
• Module9:- Working on List-Accessing, operational, Function and Methods
• Module10:- Working on Tuple- Accessing, operational, Function and Methods
• Module11:- Working on Tuple- Accessing, operational, Function and Methods
• Module12:-Working on Dictionaries- Accessing, operational, Properties, Function and Methods.
• Module13:- Implementation of Function- Definition, Calling a Function, Type of Functions.
• Module14:- Function Arguments, Anonymous Functions, Global and Local Variables.
• Module15:- Working on Modules- importing Modules, Math Module, Random Module, Packages and Compositions.
• Module16:- Working on Input/output Operations- Printing on Screen, Reading data from Keyboard.
• Module17:-Implementation of input/output Operations- Opening and closing Files, Reading and Writing Files, Functions
• Module18:- Implementations on Exception Handling
• Module19:- Implementation on OOPs Concepts- Object, class, attributes, inheritance, overloading, overriding, Data Hiding.
• Module20:- Introduction to Lambda Function, Sample Programs using lambda functions, Map and lambda functions, Sorting data with lambda functions
• Module21:- Filter and lambda functions, Reduce and lambda functions, Applying aggregate functions ,Conditional statements with lambda functions
• Module22:- Accessing nested list with lambda function and map function, Accessing dictionary keys and values using lambda functions, Filtering dictionary, Processing multiple lists
• Module23:- Introduction to Regular Expressions, RegEx Module, RegEx Functions, Search(), Split(), Sub()
• Module24:- Metacharacters, Special Sequences, Sets, Validation of Phone Number using Regular Expression.
• Module25:- Introduction to Multi-Threading, Multi-Tasking in Python, Types of Multi-tasking in OS , Thread, How to achieve multi-threading in Python, How to create Threads, Race Condition
• Module26:- Thread Synchronization, Locks, Thread Class Methods, Thread Communication.
• Module27:-Decorators, Need of Decorators, What are First Class Objects?, Treating the functions as objects, Passing the function as argument, Returning functions from another functions
• Module28:- Using decorators to wrap another function, Applying multiple decorators in a single function, Decorating functions that accepts arguments
• Module29:- What are generator functions, Generator object, Reversing a string using generators, Difference between normal functions and generators
• Module30:- Nested functions and non-local variables, When to use Closures, Example Programs using Closures.
• Module31:- Introduction Iterators, Types of iterators in Python, Iterating through iterator, Create an iterator, StopIteration, Implementing TV Remote Control System using Iterators
• Module32:- Introduction to Framework- Numpy, Scipy, Pandas, Matplotlip, Tensorflow, Keras
• Module33:- Understating Data Processing- importing libraries and data sets
• Module34:- Working on Missing data, categorial Data, Splitting data, Feature Scaling.
• Module35:- Creating data processing Template
• Module36:- Understating Regression- Simple Linear Regression, Multiple Linear Regression
• Module37:- Understating Polynomial regression, support vector regression, decision tree regression
• Module38:- Understating Classification of Logistic regression, K-nearest Neighbors, Support vector Machine
• Module39:- Understating Kernel SVM, Naive Bays, decision tree Classifier, Evaluating Classification Model Performance.
• Module40:- Understating Perception, feature expansion, method and Kernel
• Module41:- Working on Clustering (K-means , Hierarchical, Recommender)
• Module42:- Understating Association Rule Learning(Apriori)
• Module43:- Understating Reinforcement learning(Upper Confidence Bound, Thompson Sampling, Markov Decision Process)
• Module44:- Understating Natural Language Processing- tokenization, Word Embedding, Sequence Model
• Module45:- Understating Deep Learning- Artificial Neural Network, Convolutional Neural Network, Transfer learning, multiclass classification, deep Convolution Model
• Module46:- Understating Dimensionality reduction- principle component analysis, linear discrimination analysis, kernel PCA
• Module47:- Understating Model Selection and boosting- XGBoost
• Real Time Latest Project