Python with Machine Learning

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Learning Objective

Python with Machine Learning Training Program helps the trainees to learn the basic concepts of Python Language and its application with Machine Learning. The main aim is to provide the learners with an introductory and broad overview of the field of ML with the focus on its applications in real world. Supervised Machine Learning methods are used in today’s real world applications like banking, e-commerce, transportation services and many others. The module of this program covers all the theoretical and practical knowledge needed by one to learn and implement Machine Learning.

A brief preview of topics that are covered in more details in subsequent modules of the training on Python with Machine Learning is mentioned below. The training program is designed for the students of Engineering & related fields.

Fees for Summer Internship

For Online Summer Training, Softpro India has a discounted fee of INR 5,000/- as compared to the Offline Fee of INR 10,000/-. Just in INR 5,000/- the Trainees will get the best of online learning with live classes, video library and assignments for their evaluation. The discount is being granted due to the high demand of online learning and Softpro India's aim of "Bridging the Technology Gap" for all our dear Trainees.

Training Syllabus

Python Overview, About Interpreted Language, Advantages/ Disadvantages of Python pydoc., Starting Python, Interpreter Path, Using the Interpreter, Running a Python script, Using Varriables,Keywords, Built-in Functions, Strings Different Literals, Math Operators and Expressions, Writing to the Screen, String Formatting, Command Line Parameters and Flow Controls.

Lists, Tuples, Indexing & Slicing, Iterating throw a Sequences, Using Enumerate(), Operators and Keywords for Sequences, The xrange() Function, List Comprehensions, Generator Expressions, Dictionaries and Sets.

Functions, Function Parameters, Global Variables, Alternate Keys, Lambda expressions, Sorting Collections of Collection, Sorting Dictionaries, Sorting List in Place, Errors and Exception Handling, Handling multiple Exceptions, The Standard Exception hierarchy, Using Modules, The Import statements, Module Search Path, Package Installation Ways

The Sys Module, Interpreter Information, STDIO, Launching External Programs, Paths Directories and Filenames, Walking Directory Trees, Maths Function, Random Numbers, Dates and Times, Zipped Archives, Introduction to Python Classes, Defining Classes, Initializes, Instances Methods, Properties, Class Methods and DataStatic Methods, Private Methods and Inheritance, Module aliases and Regular Expression

Debugging, Dealing with Errors, Creating a Database with SQLite3, CRUD Operations, Creating a Database Object

Learning NumPy, Plotting using matplotlib and Seabron, Machine Learning Application, Introduction to Pandas, Creating Data Frames, Grouping Sorting, Plotting Data, Creating Functions, Converting Different Formats, Combining Data from Various Formats, Slicing / Dicing Operations.

Various Machine Learning Algorithm in Python, Apply Machine learning Algorithm in Python.

How to select the right Data, Which are the best feature to use, Additional features selection techniques, A Feature selection case study, Preprocessing Introduction, Preprocessing Scaling techniques, How to Preprocess your Data, How to Scale your Data, Feature Scaling Final Project

Highly Efficient machine Learning Algorithms, Bagging Decision Trees, The Power of ensembles, Random Forest Ensemble technique, Boosting - Adaboost, Boosting enesemble stochastic gradient boosting, A final ensemble technique.

Introduction Model Tuning, Parameter Tuning GridSearchCV, A Second method to tune your Algorithm, How to automate machine learning, Which ML Algo should you choose, How to compare machine learning Algorithms in practice.

Neural Networks Introduction, What is deep learning, What is one hot encoding, How to implement one hot encoding, How to handle missing values, How to impute missing Values, Introducing the MNIST dataset

Programming a neural network in tensorflow, Programming a neural network-Multilayer perceptron in tensorflow.

Introduction to keras - a convient way to code neural networks, What is a convolutional neural network, How does a cnn work

Creating a convolutional neural network from scratch, What are RNNs - Introduction to RNNs, Recurrent Neural Network rnn in python, LSTMs for Begginners - Understanding LSTMs, Long short term memory neural network LSTM in Python.

Our Training Process

20
Years Experience in this Field
Industry Academia Interaction

Bonus Lectures by Industry Expert from
Top - Notch Companies