Weekend Training on PYTHON
Class Room Python Training Starts On 14th March 2020,
Timing:- 10 AM to 2 PM
Live Python Training Starts On 21st March 2020,
Timing:- 3 PM to 7 PM
Fee : Rs 15000/- (includes GST , free Wi-Fi, Soft study Material , Tea and Lunch)
2 Days White Board Training + 2 Days On System Training
Venue : 96, Udyog Vihar Phase 1 , Gurgaon
For Registration Contact : 8766384521, 9818664846
joworkspaces@gmail.com
OR
Course Content
| Getting Started with Python |
|---|
| Installation |
| Basic Python syntax |
| Type Variables and Operators |
| Variables |
| MAGIC FUNCTIONS |
| Operators |
| Strings |
| Python strings |
| Slicing for substrings |
| Python string methods |
| String functions |
| Tuple |
| Tuple Functions |
| Operations of tuples |
| Lists |
| Creating a list |
| List Operations |
| list functions |
| list methods |
| list comprehensions |
| Dictionary |
| Operations on the dictionary |
| Dictionary Functions |
| Dictionary Methods |
| Python dictionary with for loop |
| Control Statements and Loops |
| The if and if..else statement |
| The if..elif..else statement |
| Loops |
| Functions |
| Pass by reference versus pass by value |
| Scope of variables |
| Modules and Packages |
| The import statement |
| Locating Python modules |
| Compiled Python files |
| The Python package |
| File Handling and Exceptions |
| Reading text from a file |
| Writing text to a file |
| Pickiling |
| Unpickling |
| Exceptions |
| Data visualization using Pyplot |
| Line Chart |
| Pie Chart |
| Bar Chart |
| Collections |
| Class and Objects |
| Object-oriented programming overview |
| Instance variables |
| The __init__ method |
| Class variables |
| Class Inheritance |
| Overriding methods |
| Operator Overloading |
| The class method |
| The static method |
| The private variable |
| Preparing the Data |
| Reading and writing CSV/TSV files with Python |
| Reading and writing JSON files with Python |
| Reading and writing Excel files with Python |
| Retrieving HTML pages with pandas |
| Imputing missing observations |
| Normalizing and standardizing the features |
| Exploring the Data |
| Exploring correlations between features |
| Producing histograms |
| Sampling the data |
| Splitting the dataset into training, cross-validation, and testing |
| Classification Techniques |
| Testing and comparing the models |
| Classifying with Naïve Bayes |
| Using logistic regression as a universal classifier |
| Utilizing Support Vector Machines as a classification engine |
| Classifying calls with decision trees |
| Predicting subscribers with random tree forests |
| Clustering Techniques |
| Assessing the performance of a clustering method |
| Clustering data with k-means algorithm |
| Discovering clusters with mean shift clustering model |
| Reducing Dimensions |
| Reducing the dimensions using the kernel version of PCA |
| Extracting the useful dimensions using Linear Discriminant Analysis |
| Regression Methods |
| Employing the kNN model in a regression problem |
| Time Series Techniques |
| Handling date objects in Python |
| Understanding time series data |
| Smoothing and transforming the observations |
| Filtering the time series data |
| Removing trend and seasonality |
| Forecasting the future with ARMA and ARIMA models |
| Graphs |
| Introduction |
| Handling graph objects in Python with NetworkX |
| Identifying people whose credit card details were stolen |
| Identifying those responsible for stealing the credit cards |