Data Science with Python and R Online Training

About the Course

In this course you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like Python.

Course features:

• Real-Time Case Study driven approach (5 Projects)
• Live Project
• Placement Assistance

Qualification

• Any Graduate. No programming and statistics knowledge or skills required

Duration of the course:

• 48 Hours
• Weekday -> 2 Hours (24 Classes)
• Weekend -> 3 Hours (8 Weekends)

Mode of course delivery: Online

What students will accomplish

• Master Machine Learning on Python & R
• Have a great intuition of many Machine Learning models
• Make accurate predictions
• Make powerful analysis
• Make robust Machine Learning models
• Create strong added value to your business
• Use Machine Learning for personal purpose
• Handle specific topics like Reinforcement Learning, NLP and Deep Learning
• Handle advanced techniques like Dimensionality Reduction
• Know which Machine Learning model to choose for each type of problem
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Who this course is for:

• Anyone interested in Machine Learning.
• Students who have at least high school knowledge in math and who want to start learning Machine Learning.
• Any intermediate level people who know the basics of machine learning, including the classical algorithms like
• linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
• Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.Any students in college who want to start a career in Data Science.
• Any data analysts who wnt to level up in Machine Learning.
• Any people who are not stisfied with their job and who want to become a Data Scientist.
• Any people who want to create added value to their business by using powerful Machine Learning tools.

How to learn

• The course content is broken down into weekly, manageable topics that will enable to learn through interactive online videos. In order to success you must:
• Go through our online content
• Engage with your peers in the discussion forum and weekly discussion forums
• Engage with the mentor allocated to you
• Try out real world case studies
• Complete your assignments and quizzes
• Investigate relevant and authentic real-world case studie

Data Science with Python and R Course Outline:

Introduction to Data Science with Python and R

• Why Machine Learning is the Future
• Applications of Machine Learning
• Introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst.
• Overview of the data, questions, and tools
• A conceptual introduction to the ideas behind turning data into actionable knowledge
• Practical introduction to the tools that will be used in the program like Python.
• Introduction Quiz

Data Preprocessing

• Python Basics (Datatypes,Statements,Functions,OOPS,Files,WebScraping)
• Importing the Libraries
• Importing the Dataset
• Summary of Object-oriented programming: classes & objects
• Missing Data
• Categorical Data
• Splitting the Dataset into the Training set and Test set
• Feature Scaling
• Python Numpy and Pandas.
• Data Visualization(Matplotlib and Seaborn)
• Assignment & Quiz

Descriptive and Inferential Statistics

• Why we need Descriptive and Inferential Statistics?
• Pre-requisites
• Sampling Distribution and Central Limit Theorem
• Hypothesis Testing
• Types of Error in Hypothesis Testing
• T-tests
• Different types of t-test
• ANOVA
• Chi-Square Goodness of Fit
• Regression and ANOVA
• Coefficient of Determination (R-Squared)
• Assignment & Quiz

Regression

• Simple Linear Regression
• Multiple Linear Regressions
• Polynomial Regression
• Decision Tree Regression
• Random Forest Regression
• Evaluating Regression Models Performance
• R-Squared Intuition
• Adjusted R-Squared Intuition
• Evaluating Regression Models Performance
• Assignment & Quiz

Classification

• Logistic Regression
• K-Nearest Neighbors (K-NN)
• Support Vector Machine (SVM)
• Support Vector Machines: Introduction
• Support Vector Machines: Maximum Margin Hyperplane and Kernel Trick

Kernel SVM

Naive Bayes

• Naive Bayes Classifier
• Random Variables
• Bayes Theorem
• Naive Bayes Classifier

Decision Tree Classification

• Planting the seed – What are Decision Trees?
• Growing the Tree – Decision Tree Learning
• Branching out – Information Gain
• Decision Tree Algorithms
• Titanic: Decision Trees predict Survival (Kaggle)

Random Forest Classification

• Random Forests – Much more than trees
• Back on the Titanic – Cross Validation and Random Forests

Evaluating Classification Models Performance

• False Positives & False Negatives
• Confusion Matrix
• ROC-AUC Curve.

Clustering

K-Means Clustering

• K-Nearest Neighbors
• K-Nearest Neighbors: A few wrinkles

Hierarchical Clustering

Recommendation Systems

• Recommendation Engines
• Content-Based Filtering
• Collaborative Filtering
• A Neighbourhood Model for Collaborative Filtering
• The Apriori Algorithm for Association Rules
• Code Along – What’s my favorite movie? – Data Analysis with Pandas
• Code Along – Movie Recommendation with Nearest Neighbour CF
• Code Along – Top Movie Picks (Nearest Neighbour CF)
• Code Along – Movie Recommendations with Matrix Factorization
• Code Along – AssociationRules with the Apriori Algorithm
• Assignment & quiz

Model Evolution Techniques For all types of Machine Learning.

• Confusion Matrix
• Gain and Lift Chart
• Kolmogorov Smirnov Chart
• AUC–ROC
• Gini Coefficient
• Concordant – Discordant Ratio
• Root Mean Squared Error
• Cross Validation.

Natural Language Processing (NLP)

• Natural Language Processing with NLTK
• Web Scraping with Beautiful Soup
• A Serious NLP Application: Text Auto Summarization
• Python Practical: Autosummarize News Articles
• Put it to work: News Article Classification using K-Nearest Neighbors
• Put it to work: News Article Classification using Naive Bayes Classifier
• Python Practical: Scraping News Websites
• Python Practical: Feature Extraction with NLTK
• Python Practical: Classification with KNN
• Python Practical: Classification with Naive Bayes
• Document Distance using TF-IDF
• Put it to work: News Article Clustering with K-Means and TF-IDF
• Python Practical: Clustering with K Means
• Assignment & quiz

Sentimental Analysis.

• Sentiment Analysis – What’s all the fuss about?
• ML Solutions for Sentiment Analysis – the devil is in the details
• Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)
• Regular Expressions
• Regular Expressions in Python
• Put it to work : Twitter Sentiment Analysis
• Twitter Sentiment Analysis – Work the API
• Twitter Sentiment Analysis – Regular Expressions for Preprocessing
• Twitter Sentiment Analysis – Naive Bayes, SVM and Sentiwordnet
• Assignment & quiz

Dimensionality Reduction

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA

Model Selection & Boosting

• Model Selection
• XGBoost
• GBM.
• Assignment & quiz

Deep Learning with Keras

• The Neuron
• The Activation Function
• How do Neural Networks work?
• How do Neural Networks learn?
• Gradient Descent
• Stochastic Gradient Descent
• Back propagation
• Business Problem Description
• Deep Knowledge on CNN and RNN.
• Face recognition problem.
• Advanced Sequence Models using LSTM.
• Deep Knowledge on Deep Learning on NLP.
• All kinds of RNN.
• Many Use cases using Deep Learning
• Assignment & quiz

Projects:

Covers Exploratory Data Analysis, Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, Recommender Engines, Text Mining, ANN, SVM, K means Clustering, Ensemble Machine Learning Techniques

Project-1:

• Predict House Price using Linear Regression

Project-2:

• Predict credit card defaulter using Logistic Regression

Project-3:

• Predict chronic kidney disease using KNN

Project-4:

• Predict quality of Wine using Decision Tree

Project-5:

• Case Studies in HealthCare AI, Retail AI, Finance AI

Technical Requirements

• Laptop: 4GB RAM with 500GB Hard Disk
• Google Chrome Browser

Software Required:

• Python
• R
• Anaconda

Course Summary

• Head Facilitator
• Subject expert who will guide the students through the content of the course and any challenges

Mentor

• Industry expert that will be there to guide you in terms of industry applications of the course content

Technical Support

• Available to assist with technical challenges you have in solving all tech-related challenges and concerns