The Python is one of the World’s most popular and fast growing popular language. This is the language mostly used by data scientists, software engineers, machine learning, and analytics. On learning the course, you can learn python programming language that introduces you to the fundamental concepts. However, you will be able to get comfortable with Python program coding.
Python Introduction
In this topic, we cover all the basics of Python.
Python overview
Applications of Python, scripts of UNIX / Windows
Operands, expressions, loops, and conditional statements
Values, types, variable, and command line arguments
Demo on creating hello world code, variable, demonstrating loops, and conditional statements
File and Sequence Operations
You can learn structure sequences, usage, and related operations.
Input / output functions of python files
Strings, numbers, tuples related operations
Lists, dictionaries, sets related operations
Demo on Tuple, list, dictionary, set properties related operations
Generic Scripts Of Python
In this section, we cover creating python scripts, address exceptions or errors in code, and filter or extract content.
Function parameters, Global variables, variable scope, and returning values.
Object oriented concepts, standard libraries, modules along with search paths
Import statements, ways of package installations, errors, multiple exceptional handling
Demo on functions, lambda, sorting, error & exceptions, module & packages
NumPy Matplotlib Pandas
You can learn basics of various measures, portability distributions, statistics, clear details of data visualization, and supporting libraries.
Numpy arrays and operations
Index slice and iterations along with read and write array files
Index and data structure operations of Pandas. In addition read and write data from CSV / Excel formats to Pandas
Matplaotlib library along with grids, axes, plot types
Styling, fonts, colors, and markers
Demo on Numpy, Pandas, and Matplotlib library
Data Manipulation
You will learn in detail about data manipulation
Basic data object functionalities
Merging, joins types, concatenation of data objects
Analyzing and exploring dataset
Demo on pandas function, aggregation, concatenation, merging, joining, GroupBy operations
Machine Learning With Python Introduction
You are introduced to machine learning concept.
About machine learning, use cases, process flow, categories.
Linear regression and gradient
Demo on Botson dataset linear regression
Supervised Learning 1 & 2
You learn supervised techniques learning ad implementation.
Classification along with use cases
Create Decision tree, algorithm
Confusion matrix and random forest
Naïve bayes and works
Implement naïve bayes classifier
Working of support vector machine and support
Random vs Grid search
Hyperparameter optimization
Demo on decision tree, random forest, implement of naïve bayes, SVM, and logistic regression implementation.
Dimensionality Reduction
In this section, you will learn dimensions impact within data, using compress dimension & PCA performing factor analysis is taught. You will also develop LDA model.
Dimensionality introduction and about dimensionality reduction
Factor analysis, PCA, Model of scaling dimensional, and LDA
Demo on PCA & scaling
Unsupervised Learning
Here, you learn in detail about unsupervised learning and clustering types to analyze data.
Clustering and use cases
K-means clustering and working
Optimal clustering and c-means clustering
Hierarchical clustering and working
Demo on implementing Hierarchical clustering and k-means clustering
Reinforcement Learning
You can learn developing smart algorithm and able to provide optimal solutions for agent environment interaction.
About Reinforcement Learning (RL) and why RL
RL elements
Exploitation Dilemma Vs Exploration
MDP (Markov Decision Process)
Q , V & alpha values
Algorithm – Epsilon Greedy
Demo on calculating, discounted reward, optimal quantities calculating, Q learning implementing, and optimal action setting up.
Analysis Of Time Series
In this section, you learn about analysis of time series, based on time that forecast dependent variables.
Analysis of time series
Importance and components of TSA
White noise, stationary, ACF & PACF
Demo on TSA forecasting, generate ARIMA, ACF & PACF plot, Dickey fuller test implementation, and non stationary to stationary conversion.
Association Rules & Recommendation engines
You can learn association rules and recommendation engines using apriori algorithm
About association rules and parameters
Association rule parameters calculation
Recommendation engines and working
Content based and collaborative filtering
Demo on Apriori algorithm and analysis of market basket
Boosting And model Selection
You learn selecting model, boosting in machine learning, convert weak algorithm to strong one.
Model selection and need of model selection
Boosting, algorithm work, types, and adaptive
Demo on cross validation and adaptive boosting

You also learn dimentionality reduction, unsupervised learning, reinforcement learning, time series analysis, model selection along with boosting, association mining rules & recommendation systems.