Python Course Details

Python is a general-purpose programming language that is becoming more and more popular in the field of data science. With the fast growth in the IT industry or extensive research in the field of Artificial intelligence, there is a surge in the demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Companies worldwide are using Python to get insights from their data and get a competitive edge.

This course in Python will equip you with all the necessary tools and commands required to master data analysis using Python. This course is a specially designed series; which starts with basics of Python and eventually covers all the necessary libraries to master the Python while keeping focus on data science.

Course Curriculum

Introduction To Python

Python Basics | Spyder IDE | Jupyter Notebook | Floats and Strings Simple Input & Output | Variables | Single and Multiline Comments

Control Structures

Booleans and Comparisons | Conditional Statements (IF ELSE) | Operator Precedence | Lists – Operations and Functions

Functions and Modules

Function Arguments | Comments and Doc Strings | Functions as Objects Modules | Standard Lib and Pip

Exceptions and Files

Exception Handling | Raising Exceptions | Assertions | Working With File

Linear Regression

Implementing Simple & Multiple Linear Regression with Python | Making Sense of Result Parameters | Model Validation | Handling Other Issues/Assumptions in Linear Regression: Handling Outliers, Categorical Variables, Autocorrelation, Multicollinearity, Heteroskedasticity | Prediction and Confidence Intervals | Use Cases

Logistics Regression

Implementing Logistic Regression with Python | Making Sense of Result Parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test | Goodness of Fit Measures | Model Validation: Cross Validation, ROC Curve, Confusion Matrix | Use Cases

Decision Trees

Implementing Decision Trees using Python | Homogeneity | Entropy Information Gain | Gini Index | Standard Deviation Reduction | Vizualizing & Prunning a Tree | Implementing Random Forests using Python | Random Forest Algorithm | Important hyper-parameters of Random Forest for tuning the model | Variable Importance | Out of Bag Errors


Introduction to Pandas | IO Tools | Basics of NumPy | NumPy Functions Pandas – Series and Data frames,

Scikit Learn

Introduction to SciKit Learn | Load Data into Scikit Learn | Run Machine Learning Algorithms Both for Unsupervised and Supervised Data | Supervised Methods: Classification & Regression | Unsupervised Methods: Clustering, Gaussian Mixture Models | Decide What’s the Best Model for Every Scenario.

3 Projects

Linear + Logistics + Decision trees

Introduction To Machine Learning

What is Machine Learning? | End-to-end Process of Investigating Data Through a Machine Learning Lens | Evolution and Trends | Application of Machine Learning | Best Practices of Machine Learning

Machine Learning Algorithims

Classification | Regression | Collaborative Filtering | Clustering Principal Component Analysis

Neural Networks

Understanding Neural Networks | The Biological Inspiration | Perceptron Learning & Binary Classification | Backpropagation Learning | Learning Feature Vectors for Words | Object Recognition


Keras for Classification and Regression in Typical Data Science Problems | Setting up KERAS | Different Layers in KERAS | Creating a Neural Network Training Models and Monitoring | Artificial Neural Network


Introducing Tensorflow | Neural Networks using Tensorflow | Debugging and Monitoring | Convolutional Neural Networks | Unsupervised Learning

2 Projects