practitioner's approach

Python

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Course Overview

Python is a very popular general purpose, high level programming language which can be used for web development, software development, mathematics, system scripting etc. At Opine, we use complete hands-on-approach, provides lots of classroom and home assignments, teach basic mathematical and statistical concepts required which makes our students fully equipped for diving in the job market.

What You Will Learn

Accomplish multi-step tasks like sorting or looping using tuples

Create programs that are able to read and write data from files

Store data as key/value pairs using Python dictionaires

Use variables to store, retrieve and calculate information

Course Modules

Module 1. Basic Python Programming 

Hours: 50

Sr. No.TopicDescription
1Setting Up Python in WindowsIntroduction to Different Environments and Notebooks
2Numerators, Operators and Comments and BooleanThe difference between = and IS
3Data TypesString Concatenation, Changing Data Types
4Conditional LogicIf-Else, Working with cases, the first python game
5Loops - While and ForAllowing the user to play the game as many times as they want
6ListsIndexing, methods and storage
7DictionariesIndexing, methods and storage
8TuplesIndexing, methods and storage
9FunctionsDeveloping your own functions and working with in-built functions
10Debugging and Error HandlingDifference between error in logic and syntactical errors
11Object Oriented ProgrammingObjects and working with them
12Iterators and GeneratorsWorking with Loops, Creating our own version of loops
13Reg-ExWildcards
14SQL embedding in PythonBasic SQL and connections

Module 2. Machine Learning Using Python

Hours: 35

Sr. No.TopicDescription
1Setting Up Jupyter notebook
2NumpyArrays, Indexing in Arrays
3PandasData Transformations, Working With Data
4Working With Data – 1JSON, HTML and Excel Data
5Working With Data – 2Data Frames, Index, Outliers, Merging, Aggregations, Grouping Data Frames
6SciKitIntroduction to ML
7Statistics – ConceptsDescriptive Statistics (Variances, Standard Deviations, Co-variances, Bi-Variate Regression (Linear), Multi-Variate Regression (Linear)
8Linear RegressionPython Application
9Multiclass Classificaion – 1Logistic Regression
10Multiclass Classificaion – 2k - Nearest Neighbour
11Vector MachinesSupervised Learning Models
12Naïve BayesProbabilistic Classifiers and Maximum Likelihood
13Decision Trees and Ranom ForestClassifications and Randomised Forests
14Introduction To Keras, TensorFlowRandomised Machine Learning Algorithms

Module 3. Visualisations Using Python

Hours: 30

Sr. No.TopicDescription
1Setting Up Jupyter notebook
2NumpyArrays, Indexing in Arrays
3PandasData Transformations, Working With Data
4Working With Data – 1JSON, HTML and Excel Data
Working With Data – 2Data Frames, Index, Outliers, Merging, Aggregations, Grouping Data Frames
5SeabornUnderstanding the Wrapper
6HistogramVisualisation Objects
7Kernel Density Estimate PlotsVisualisation Objects
8Density plotsVisualisation Objects
9ChartsVisualisation Objects
10Regression PlotsVisualisation Objects
11Heat-mapsVisualisation Objects
12Clustered MatricesVisualisation Objects

Highlights

  • Highly Qualified Trainers from Industry
  • Practitioner’s Approach
  • Assignments
  • Projects
  • Weekdays/Weekend Batches Available
  • Flexible Timings
  • Classroom / Online Training Available
  • Assistance For : Resume Writing / Interview Preparation

Student's Testimonials