Learn Data Science, Deep Learning & Machine Learning with Python

Live Machine Learning & Deep Learning Projects 

Introduction :

This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

Data Science, Statistics with Python :

This course Start with  introduction to Data Science and Statistics using Python. It covers both the  aspects of Statistical concepts and the practical implementation using  Python. If you’re new to Python, don’t worry – the course starts with a crash course to teach you all basic programming concepts. If you’ve done some programming before or you are new in Programming, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems.

Analytics :

Using Spark and Scala you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Data frames to manipulate data with ease.

Machine Learning and Data Science :

Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

Real Life examples :

Every concept is explained with the help of examples, case studies and source code wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant. finance context. 

Target Audience :

• Engineering/Management Graduate or Post-graduate Fresher Students who want to make their career in the Data Science Industry or want to be future Data Scientists.
• Engineers who want to use a distributed computing engine for batch or stream processing or both
• Analysts who want to leverage Spark for analyzing interesting datasets
• Data Scientists who want a single engine for analyzing and modelling data
• MBA Graduates or business professionals who are looking to move to a heavily quantitative role.
• Engineering Graduate/Professionals who want to understand basic statistics and lay a foundation for a career in Data Science
• Working Professional or Fresh Graduate who have mostly worked in Descriptive analytics or not work anywhere and want to make the shift to being  data scientists
• Professionals who’ve worked mostly with tools like Excel and want to learn how to use Python  for statistical analysis.

COURSE CONTENT :

Introduction to Data Science with Python

• What is analytics & Data Science?
• Common Terms in Analytics
• Analytics vs. Data warehousing, OLAP, MIS Reporting
• Relevance in industry and need of the hour
• Types of problems and business objectives in various industries
• How leading companies are harnessing the power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem solving framework
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Project plan for Analytics project & key milestones based on effort estimates
• Build Resource plan for analytics project

Python Essentials

• Why Python for data science?
• Overview of Python- Starting with Python
• Introduction to installation of Python
• Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
• Understand Jupyter notebook & Customize Settings
• Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
• Installing & loading Packages & Name Spaces
• Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
• List and Dictionary Comprehensions
• Variable & Value Labels –  Date & Time Values
• Basic Operations – Mathematical – string – date
• Reading and writing data
• Simple plotting
• Control flow & conditional statements
• Debugging & Code profiling
• How to create class and modules and how to call them?

Scientific Distributions Used In Python For Data Science

NumPy, pandas, scikit-learn, stat models, nltk

Accessing/Importing And Exporting Data Using Python Modules  

• Importing Data from various sources (Csv, txt, excel, access etc)
• Database Input (Connecting to database)
• Viewing Data objects – subsetting Data, methods
• Exporting Data to various formats
• Important python modules : Pandas, beautiful soup

Data Manipulation – Cleansing – Munging using python modules

• Cleansing Data with Python
• Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
• Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
• Python Built-in Functions (Text, numeric, date, utility functions)
• Python User Defined Functions
• Stripping out extraneous information
• Normalizing data
• Formatting data
• Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc.)

Data Analysis – Visualization Using Python

• Introduction exploratory data analysis
• Descriptive statistics, Frequency Tables and summarization
• Univariate Analysis (Distribution of data & Graphical Analysis)
• Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
• Creating Graphs – Bar/pie/line chart/histogram/boxplot/scatter/density etc.)
• Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and SciPy. Stats etc.)

Introduction to Statistics

• Basic Statistics – Measures of Central Tendencies and Variance
• Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
• Inferential Statistics -Sampling – Concept of Hypothesis Testing Statistical Methods – Z/t-tests (One sample, independent, paired), Analysis of variance, Correlations and Chi-square
• Important modules for statistical methods : NumPy, SciPy, Pandas

Introduction to Predictive Modelling

• Concept of model in analytics and how it is used?
• Common terminology used in analytics & Modelling process
• Popular modelling algorithms
• Types of Business problems – Mapping of Techniques
• Different Phases of Predictive Modelling

Data Exploration For Modelling

• Need for structured exploratory data
• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
• Identify missing data
• Identify outliers data
• Visualize the data trends and patterns

Data Preparation

• Need of Data preparation
• Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
• Variable Reduction Techniques – Factor & PCA Analysis

Segmentation : Solving Segmentation Problems

• Introduction to Segmentation
• Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
• Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
• Behavioural Segmentation Techniques (K-Means Cluster Analysis)
• Cluster evaluation and profiling – Identify cluster characteristics
• Interpretation of results – Implementation on new data

Linear Regression : Solving Regression Problems

• Introduction – Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
• Assess the overall effectiveness of the model
• Validation of Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
• Interpretation of Results – Business Validation – Implementation on new data

Logistic Regression : Solving Classification Problems

• Introduction – Applications
• Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
• Building Logistic Regression Model (Binary Logistic Model)
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
• Validation of Logistic Regression Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
• Interpretation of Results – Business Validation – Implementation on new data

Time Series Forecasting : Solving Forecasting Problems

• Introduction – Applications
• Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
• Classification of Techniques (Pattern based – Pattern less)
• Basic Techniques – Averages, Smoothening, etc
• Advanced Techniques – AR Models, ARIMA, etc
• Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Machine Learning : Predictive Modelling

• Introduction to Machine Learning & Predictive Modelling
• Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
• Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
• Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
• Overfitting (Bias-Variance Trade off) & Performance Metrics
• Feature engineering & dimension reduction
• Concept of optimization & cost function
• Overview of gradient descent algorithm
• Overview of Cross validation(Bootstrapping, K-Fold validation etc)
• Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)

Unsupervised Learning : Segmentation

• What is segmentation & Role of ML in Segmentation?
• Concept of Distance and related math background
• K-Means Clustering
• Expectation Maximization
• Hierarchical Clustering
• Spectral Clustering (DBSCAN)
• Principle component Analysis (PCA)

Supervised Learning : Decision Trees

• Decision Trees – Introduction – Applications
• Types of Decision Tree Algorithms
• Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
• Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
• Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
• Decision Trees – Validation
• Overfitting – Best Practices to avoid

Supervised Learning : Ensemble Learning

• Concept of Ensembling
• Manual Ensembling Vs. Automated Ensembling
• Methods of Ensembling (Stacking, Mixture of Experts)
• Bagging (Logic, Practical Applications)
• Random forest (Logic, Practical Applications)
• Boosting (Logic, Practical Applications)
• Ada Boost
• Gradient Boosting Machines (GBM)
• XGBoost

Supervised Learning : Artificial Neural Network – ANN

• Motivation for Neural Networks and Its Applications
• Perceptron and Single Layer Neural Network, and Hand Calculations
• Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
• Neural Networks for Regression
• Neural Networks for Classification
• Interpretation of Outputs and Fine tune the models with hyper parameters
• Validating ANN models

Supervised Learning : Support Vector Machines

• Motivation for Support Vector Machine & Applications
• Support Vector Regression
• Support vector classifier (Linear & Non-Linear)
• Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
• Interpretation of Outputs and Fine tune the models with hyper parameters
• Validating SVM models

Supervised Learning : KNN

• What is KNN & Applications?
• KNN for missing treatment
• KNN For solving regression problems
• KNN for solving classification problems
• Validating KNN model
• Model fine tuning with hyper parameters

Supervised Learning : Naive Bayes

• Concept of Conditional Probability
• Bayes Theorem and Its Applications
• Naïve Bayes for classification
• Applications of Naïve Bayes in Classifications

Text Mining And Analytics

• Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD); Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
• Finding patterns in text: text mining, text as a graph
• Natural Language processing (NLP)
• Text Analytics – Sentiment Analysis using Python
• Text Analytics – Word cloud analysis using Python
• Text Analytics – Segmentation using K-Means/Hierarchical Clustering
• Text Analytics – Classification (Spam/Not spam)
• Applications of Social Media Analytics
• Metrics(Measures Actions) in social media analytics
• Examples & Actionable Insights using Social Media Analytics
• Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
• Fine tuning the models using Hyper parameters, grid search, piping etc.

AI WITH MACHINE & DEEP LEARNING

COURSE CONTENT :

Introduction with Artificial Intelligence

• What is AI (Artificial Intelligence) ?
• What types of intelligences we are talking about?
• Different definitions and Ultimate goal of AI.
• What are application areas for AI?
• History of AI and some real life examples of AI.

ML and other related terms to AI

• What is ML and How it is related with AI?
• What is NLP and How it is related with AI?
• What is DL and How it is related with ML and AI?
• What are ANNs and DNNs and How are they related to AI?

A working example of AI and ML

Project -1:

These simple tasks are to make you understand how AI and ML can find their applications in real life.

Python libraries for ML

• What are Libraries, packages and Modules?
• What are top Python libraries for ML in Python?

Setting up Anaconda development environment

• Why choosing Anaconda development environment?
• Setting up Anaconda development environment on Windows 10 PC.
• Verifying proper installation of Anaconda environment.

Getting into core development of ML

• What is a classifier in ML?
• Important elements and flow of any ML projects.
• Let’s develop our first ML program – explanations
• Let’s develop our first ML program – development

Project – 2 :

These simple tasks are going to give you some great experience with Machine Learning introductory programs or better say, “Hello world” programs of Machine Learning.

Different ML techniques

• What all ML techniques are there?
• Evaluation methods of all ML techniques.

Developing complete project of ML (IRIS flower project)

• Developing complete ML project – understanding data set
• Developing complete ML project – understanding flow of project
• Developing complete ML project – visualizing data set through Python
• Developing complete ML project – development
• Developing complete ML project – concepts explanations
• Developing another project of ML (Digit recognition project)

Project – 3 :

• After completing these project, you have done and understood multiple complete projects of Machine Learning.

Introduction of Ai with Deep Learning

• Installation
• CPU Software Requirements
• CPU Installation of PyTorch
• PyTorch with GPU on AWS
• PyTorch with GPU on Linux
• PyTorch with GPU on MacOSX

PyTorch Fundamentals : Matrices

• Matrix Basics
• Seed for Reproducibility
• Torch to NumPy Bridge
• NumPy to Torch Bridge
• GPU and CPU Toggling
• Basic Mathematical Tensor Operations
• Summary of Matrices

PyTorch Fundamentals : Variables and Gradients

• Variables
• Gradients
• Summary of Variables and Gradients

Linear Regression with PyTorch

• Linear Regression Introduction
• Linear Regression in PyTorch
• Linear Regression From CPU to GPU in PyTorch
• Summary of Linear Regression

Logistic Regression with PyTorch

• Logistic Regression Introduction
• Linear Regression Problems
• Logistic Regression In-depth
• Logistic Regression with PyTorch
• Logistic Regression From CPU to GPU in PyTorch
• Summary of Logistic Regression

Feedforward Neural Network with PyTorch

• Logistic Regression Transition to Feedforward Neural Network
• Non-linearity
• Feedforward Neural Network in PyTorch
• More Feedforward Neural Network Models in PyTorch
• Feedforward Neural Network From CPU to GPU in PyTorch
• Summary of Feedforward Neural Network

Convolutional Neural Network (CNN) with PyTorch

• Feedforward Neural Network Transition to CNN
• One Convolutional Layer, Input Depth of 1
• One Convolutional Layer, Input Depth of 3
• One Convolutional Layer Summary
• Multiple Convolutional Layers Overview
• Pooling Layers
• Padding for Convolutional Layers
• Output Size Calculation
• CNN in PyTorch
• More CNN Models in PyTorch
• CNN Models Summary
• Expanding Model’s Capacity
• CNN From CPU to GPU in PyTorch
• Summary of CNN

Recurrent Neural Networks (RNN)

• Introduction to RNN
• RNN in PyTorch
• More RNN Models in PyTorch
• RNN From CPU to GPU in PyTorch
• Summary of RNN

Long Short-Term Memory Networks (LSTM)

• Introduction to LSTMs
• LSTM Equations
• LSTM in PyTorch
• More LSTM Models in PyTorch
• LSTM From CPU to GPU in PyTorch
• Summary of LSTM

Projects :

Deep Learning Projects

Machine Learning Projects