Best Online Machine learning Training Institute In Delhi (Noida).

Course Content

Introduction to Machine learning using Python • What is Machine learning, what does a data scientist do • Various examples of Machine learning in the industries • How Python is deployed for Machine learning applications • Various steps in Machine learning process and selecting the model. • Introduction to Python programming language • Important Python features, how is Python different from other programming languages • Python installation, Anaconda Python distribution for Windows, Linux and Mac • How to run a sample Python script, Python IDE working mechanism • Running some Python basic commands • Python variables, data types and keywords. Hands-on Exercise – • Installing Python Anaconda for the Windows, Linux and Mac Module 02: Python basic constructs • Introduction to a basic construct in Python • Understanding indentation like tabs and spaces • Python built-in data types • Basic operators in Python • Loop and control statements like break, if, for, continue, else, range () and more. Hands-on Exercise – • Write your first Python program • Write a Python function (with and without parameters) • Use Lambda expression • Write a class • Create a member function and a variable • Create an object and write a for loop to print all odd numbers Module 03: NumPy for mathematical computing • Introduction to mathematical computing in Python • What are arrays and matrices, array indexing, array math, Inspecting a NumPy array, NumPy array manipulation Hands-on Exercise – • How to import NumPy module • Creating array using ND-array • Calculating standard deviation on array of numbers and calculating correlation between two variables. Module 04: Pandas for Data manipulation • What is a data Manipulation? Using Pandas library • NumPy dependency of Pandas library • Series object in pandas • DataFrame in Pandas • Loading and handling data with Pandas • How to merge data objects • Concatenation and various types of joins on data objects, exploring dataset Hands-on Exercise – • Doing data manipulation with Pandas by handling tabular datasets that includes variable types like float, integer, double and others. • Cleaning dataset, Manipulating dataset, Visualizing dataset Module 05: Data visualization with Matplotlib • Introduction to Matplotlib • Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more • Matplotlib API Hands-on Exercise – • Deploying Matplotlib for creating pie, scatter, line and histogram. • Subplots and Pandas built-in data visualization. Module 06: Maths for DS-Statistics & Probability • Central Tendency • Variability • Hypothesis Testing • Anova • Correlation • Regression • Probability Definitions and Notation • Joint Probabilities • The Sum Rule, Conditional Probability, and the Product Rule • Bayes Theorem Hands-on Exercise – • We will analyze both categorical data and quantitative data • Focusing on specific case studies to help solidify the week’s statistical concepts Module 07: Machine Learning using Python • Revision of topics in Python (Pandas, Matplotlib, NumPy, scikit-Learn) • Introduction to machine learning • Need of Machine learning • Types of machine learning and workflow of Machine Learning • Uses Cases in Machine Learning, its various algorithms • What is supervised learning • What is Unsupervised Learning Hands-on Exercise – • Demo on ML algorithms Module 08: Supervised learning-Linear Regression • What is linear regression • Step by step calculation of Linear Regression • Linear regression in Python Hands-on Exercise – • Using Python library Scikit-Learn for coming up with Random Forest algorithm to implement supervised learning. Module 09: Supervised learning-Logistics Regression • Logistic Regression • What is classification • Decision Tree, Confusion Matrix, Random Forest, Naïve Bayes classifier, Support Vector Machine, XGBoost Hands-on Exercise – • Using Python library Scikit-Learn for coming up with Random Forest algorithm to implement supervised learning. Module 10: Unsupervised Leaning & K Means Clustering • Introduction to unsupervised learning • Use cases of unsupervised learning • What is clustering • Types of clustering-Exclusive clustering, Overlapping Clustering, Hierarchical Clustering • What is K-means clustering • Step by step calculation of k-means algorithm • Association Rule Mining, Market Basket Analysis, Measures in association rule mining-support, confidence, lift • Apriori Algorithm Hands-on Exercise – • Setting up the Jupyter notebook environment • Loading of a dataset in Jupyter • Algorithms in Scikit-Learn package for performing Machine Learning techniques and training a model to search a grid. • Practice on k-means using Scikit • Practice on Apriori Module 11: Hire Clustering And Dimension Reduction • Introduction to Dimensionality • Why Dimensionality Reduction • PCA • Factor Analysis • LDA Hands-on Exercise – • Practice Dimensionality reduction Techniques : PCA, Factor Analysis, t-SNE, Random Forest, Forward and Backward feature Module 12: Time Series Forecasting • White Noise • AR model • MA model • ARMA model • ARIMA model • Stationarity • ACF & PACF Hands-on Exercise – • Create AR model • Create MA model • Create ARMA model Module 13: OOPs in Python • Understanding the OOP paradigm • What are access modifiers, instances, class members • Classes and objects • Function parameter and return type functions • Lambda expressions. Hands-on Exercise – • Writing a Python program and incorporating the OOP concepts Module 14 : Python integration with Spark • Introduction to PySpark • Who uses PySpark, need of spark with python • PySpark installation • PySpark fundamentals • Advantage over MapReduce, PySpark • Use-cases PySpark and demo. Hands-on Exercise – • Demonstrating Loops and Conditional Statements • Tuple – related operations, properties, list, etc. • List – operations, related properties • Set – properties, associated operations, dictionary – operations, related properties