Description
An “intelligent” computer uses AI to think like a human and perform tasks on its own. Machine learning is how a computer system develops its intelligence. One way to train a computer to mimic human reasoning is to use a neural network, which is a series of algorithms that are modeled after the human brain.
Course Content
Source code Vs Bytecode Vs Machine code
Compiler Vs Interpreter
C/C++, Java Vs Python
Code Editors Basics
Different type of code editors in python
Introduction to Anaconda and IDEs
Python Basics
Variable Vs Identifiers
Strings Operators Vs Operand
Procedure-oriented Vs Modular programming
Measures of Central Tendency & Dispersion
Inferential statistics and Sampling theory
Chapter 1: Programming Introduction
- What are the different types of programming languages?
- What is Compiler?
- What is an Interpreter?
Chapter 2: Python Introduction
- How does a Python Program runs on our system?
- Features of Python Memory Management in Python,
- Different Implementations of Python.
Chapter 3: Conditional And Loops
- Conditional Statement
- Loop Statement
Chapter 4: Python Programming Components
- Linting, Formatting,Understanding Python code
- Command Line Arguments,Python Operators
Chapter 5: Functions
- Working with functions,Parameters vs Arguments
- Namespace vs Scope,Function call vs Function referencing
Chapter 6: Exception Handling
- Introduction to Exception Handling,Type of Errors
- Nestedtry-except block & Default except for block.
Chapter 7: Modules in Python
- Introduction to Modular Programming
- Importing Modules and different import statement,Types of Modules.
Chapter 8: File Handling
- What is File Handling?, Why do we need File Handling?
- Use of File Handling, Type of Files, File Operation
Chapter 9: Regular Expressions
- Intro & use of Regular Expression, Regex module & important methods,
- Regex pattern and it's interpretation
Chapter 10: Numpy in Python
- Intro & use of numpy, What is an array?
- Array Operations using Numpy, Numpy and Scipy, Numpy and Pandas
Chapter 11: Pandas in Python
- Numpy vs Pandas, Exporting Dataframe to CSV and Excel
- EDA using Pandas
Chapter 12: MatplotLib
- Lines & markers, Figures, Watermark, Shapes
- Polygons and arrows Color maps, Autocorrelation study
Chapter 13: Seaborn
- Working with seaborn on titanic dataset, Introduction & installation
- Controlling figure asthetics, Different plots in seaborn
Chapter 14: Other visualisation libraries
- Plotly, Pygal, Geopitlib etc.
Chapter 1: Probability Theory and Statistical Inferences
- Introduction to Probability Principles,Random Variables and Probability principles
- Discrete Probability Distributions - Binomial, Poisson etc,Continuous Probability Distributions - Gaussian, Normal, etc
- Joint and Conditional Probabilities, Bayes theorem and its applications
- Central Limit Theorem and Applications
Chapter 2: Statistics Foundations
- Elements of Descriptive Statistics, Measures of Central tendency and Dispersion
- Inferential Statistics fundamentals,Sampling theory and scales of measurement, Covariance and correlation
Chapter 3: Hypothesis Testing and its Applications
- Basic Concepts - Formulation of Hypothesis, Making a decision
- Advanced Concepts - Choice of Test - t test vs z test
- Evaluation of Test - P value and Critical Value Approach
- Confidence Intervals, Type 1 and 2 errors
Chapter 4: Exploratory Data Analysis and the Art of Storytelling
- Ingest data,Data cleaning
- Outlier detection & Missing value imputation
- Missing value imputation, Capstone project for Business Analysis
Chapter 1: A primer on Machine Learning
- Types of Learning - Supervised, Unsupervised, and Reinforcement, Statistics vs Machine Learning
- Types of Analysis - Descriptive, Predictive, and PrescriptiveBias Variance Tradeoff - Overfitting vs Underfitting
- Bias Variance Tradeoff - Overfitting vs Underfitting
Chapter 2: Regression
- Correlation vs Causation,Simple and Multiple linear regression
- Linear regression with Polynomial features,What is linear in Linear Regression?
- OLS Estimation and Gradient descent, Model Evaluation Metrics for regression problems - MAE, RMSE, MSE, and MAP
Chapter 3: Classification
- Introduction to Classification problems, Logistic Regression for Binary problems
- Maximum Likelihood estimation, Data Imbalance and redressal methodology
- Upsampling, Downsampling and SMOTE
Chapter 4: Clustering-K means
- Introduction to Unsupervised Learning, Hierarchical and Non-Hierarchical techniques
- K Means Algorithms - Partition-based model for clustering,Model Evaluation metrics – Clustering
Chapter -5: KNN
- Introduction to KNNs, KNNs as a classifier
- Non-Parametric algorithms and Lazy learning ideology
- Applications in Missing value imputes and Balancing datasets
Chapter 6: Advance Regression Model
- Introduction to regularization,Understanding ridge regression
- Working with Lasso regression,Tackling multicollinearity with regression
Chapter 7: Decision Trees
- Nonlinear models for classification, Intro to decision trees
- Why are they called Greedy Algorithms?,Information Theory - Measures of Impurity
Chapter 8: Ensemble Techniques
- Introduction to Bagging as an Ensemble technique, Bootstrap Aggregation and Out of Bag error
- Random Forests and its Applications in Feature selection, How Bagging overcomes the overfitting problem?
- Scent and Boosting, How Boosting overcomes the Bias - Variance Tradeoff
- Gradient Boosting and Xgboost as regularised boosting
Chapter 9: Support Vector Machines
- Introduction to Expectation- Maximization Algorithms,The kernel tricks
- Linear, Polynomia, and RBF kernels, SMs for regression and classification
- Applications on multi class classification
Chapter 10: Bayesian Family Algorithms
- Naive Bayes for Text classification, Bag of words and TF-IDF algorithm
- Multinomial and Gaussian Naive Bayes, Bayesian Belief networks and Path models.
Chapter 11: Time-schies Analysis
- Intro to Time series, Autocorrelation and AC/PAC plots
- The Random Walk model and Stationarity of Time Series
- Tests for Stationarity - ADF and Dickey- Fuller test, AR, MA, ARIMA, SARIMA models
- A regression approach to time series forecasting.
Chapter 12: Machine Learning Pipeline & auto ML
- Feature engineering & selection techniques
- Principal Component Analysis, Linear Discriminant Analysis
- Serving the model via Rest API & Keras.
Chapter -1 - neural networks
- Introduction to Neural Networks, Layered Neural networks
- Activation Functions and their application,Backpropagation and Gradient Descent
Chapter 2: Tensor Flow
- Introduction to TensorFlow,Working with TensorFlow
- Linear regression with TensorFlow,Logistic regression with TensorFlow
Chapter 2: Tensor Flow
- Introduction to TensorFlow,Working with TensorFlow
- Linear regression with TensorFlow,Logistic regression with TensorFlow
Chapter 3: Deep Neural Networks
- Designing a deep neural network, Optimal choice of Loss Function
- Tools for deep learning models - Tflearn and Pytorch,The problem of Exploding and Vanishing gradients
Chapter 4: Convolutional Neural networks.
- Architecture and design of a Convolutional network
- Deep convolutional models & image augmentation
Chapter 5: Recurrent Neural networks and LSTMs
- RNN & LSTM structure, Bidirectional RNNs
- Applications on Sequential data, Advanced Time series forecasting using RNNs with STMs,LSTMs vS GRUs
Chapter 6: Restricted Boltzmann Machines and Autoencoders.
- Intro to RBMs, Autoencoders, Application of RBMs in Collaborative filtering
- Autoencoders for Anomaly detection, Capstone Project - Self-driving cars, Facial recognition
Chapter 1: Language Modelling
- Intro to the NLTK library, N-gram Language models: Perplexity and Smoothing
- Introduction to Hidden Markov models, Viterbi algorithms,MEMMs and CRFs for named entity recognition.
- Neural Language models,Application of STMs to predict the next word
Chapter 2: Vector Space Models
- Explicit and Implicit matrix factorization,Word2vec and Doc2vec models
Chapter 3: Sequence to Sequence tasks
- Introduction to Machine translation,Natural language processing
- LP with machine translation for text analysis
- Word Alignment models,Encoder-Decoder Architecture
- How to implement a conversational Chatbot
Chapter 4: Capstone Project
- Fully functional chatbot
- Front end, backend, and deployment process for chatbot
Array basics, Problem-solving techniques with example
- Time Complexity & Bit manipulations,Sorting, Searching & String Algorithms
- Linked list,Two pointer techniques,Stack & Queue - Implementation & Problems
- Tree, Trie, Ternary Search tree,Recursion & Greedy Algorithms
- Combinatorial problems with backtracking,Hashing, Graph Theory,Dynamic Programming
Topics Covered
Chapter 1: Excel Fundamentals
- Introduction to Excel interface,Customizing Excel Quick Access Toolbar, Structure of Excel Workbook,Excel Menus
- Excel Toolbars: Hiding, Displaying, and Moving Toolbars, Switching Between Sheets in a Workbook,Inserting and Deleting Worksheets
- Renaming and Moving Worksheets, Protecting a Workbook, Hiding and Unhiding Columns, Rows and Sheets,Splitting and Freezing a Window
- Inserting Page Breaks, Advanced Printing Options,Opening, saving, and closing Excel document,Common Excel Shortcut Keys, Quiz
Chapter 2: Worksheet Customization
- Adjusting Page Margins and Orientation,Creating Headers, Footers, and Page Numbers, Adding Print Titles and Gridlines,Formatting Fonts & Values
- Adjusting Row Height and Column Width,Changing Cell Alignment,Adding Borders,Applying Colours and Patterns
- Using the Format Painter,Formatting Data as Currency Values, Formatting Percentages,Merging Cells, Rotating Text
- Using Auto Fill, Moving and Copying Data in an Excel Worksheet,Inserting and Deleting Rows and Columns
Chapter 3: Images and Shapes into Excel Worksheet
- Inserting Excel Shapes, Formatting Excel Shapes
- Inserting Images, Working with Excel SmartArt
Chapter 4: Basic work on Excel
- Entering and selecting values. Using numeric data in excel
- Working with forms menu, cell references, conditional, Formatting and data validation
- Finding and replacing information from worksheet,inserting & deleting cells, rows, and columns
Chapter 5: Excel Formulae
- Creating basic formulae in excel, Implementing excel formulae in the worksheet
- Relative cell referencing, Absolute cell referencing, Relative vs Absolute cell references in formulae
Chapter 6: Excel Functions
- Working with functions like SUM, AVERAGE, etc
- Adjacent cells error in excel calculations,Use of AutoSum & autofill command
Chapter 7: Working with Charts and Graphs
- Creating a column chart,Working with the excel chart ribbon
- Adding and modifying data on an Excel chart,Formatting an excel chart
- Moving a chart to another worksheet
- Resizing a chart,Changing a chart's source data
- Adding titles, gridlines, and a data table
- Formatting a data series and chart axis, Using fill effects
- Changing a chart type and working with pie charts, Quiz
Chapter 6: Exception Handling
- Introduction to Exception Handling,Type of Errors
- Nestedtry-except block & Default except for block.
Chapter 8: Support Vector Machines
- Intro to Pivot Tables, Structuring Source Data for Analysis in Excel
- Creating a PivotTable, Exploring Pivot Table Analyse & Design Options
- Working with and on pivot tables,Dealing with Growing Source Data
- Enriching data with Pivot table calculated values & fields
- Formatting and charting a PivotTable,Pivot Table Case Study,Quiz
Chapter 9: Basic Macros
- Introduction to macros, Automating Tasks with Macros
- Recording a Macro, Playing a Macro
- Assigning a Macro a Shortcut Key
Chapter 10: Introduction to SQL
- What is a Database?,Why SQL?
- All about SQL Difference between SQL & MongoDB
- Different Structured Query languages,Installation of MySQL
- DDL,SQL Keywords,DCL,TCL, Database Vs Excel Sheets,Relational and database schema
- Foreign and Primary Keys,Database manipulation, management, and administration
Chapter 11: No SQL Databases
- What is HBase?,HBase Architecture,HBase Components, Storage Model of HBase,HBase vs RDBMS,Introduction to Mongo DB, CRUD
- Advantages of MongoDB over RDBMS,Use cases,First Step in SQL Database
- Creating Database,Dropping Database,Using Database,Introduction to Tables
- Data types in SQL,Creating a table,Dropping table,Coding best practices in SQL
Chapter 12: SQL Databases
- Introduction to database,Creating & Dropping Database
- Using Database,Introduction to Tables,Data types in SOL,Use case of different data
- Working with tables,Coding best practices in SQL
Chapter 13: SQL Databases
- SELECT Statement,COUNT,SELECT WHERE,ORDER BY,IN, NOT IN
- NULL and NOT_NULL, Comparison Operators (=, >, >=, <=),MySQL Warnings (Understand and Debug)
Chapter 14: Refining Selection
- SELECT DISTINCT,IKE, NOT LIKE, ILIKE
- LIMIT,BETWEEN, BETWEEN - AND
Chapter 15: SQL Statements & Functions
- Multiple INSERT,NSERT INTO
- GROUP BYHAVING, WHERE VS HAVING,UPDATE, DELETE, AS,EXISTS-NOT EXISTS
- Aggregator functions,Application of group by,Count function, MIN and MAX,Sum Function,Avg Function
Chapter 16: JOINS & Functions
- Introduction to JOINs, Types of JOINS,Usage of different types of JOINS,Loading Data
- Usage of string functions like; CONCAT, SUBSTRING, etc
- INNER join,OUTER join,Full join, Left Join, Right Join & UNION
Chapter 17: Advance SQL
- Local, Session, Global Variables,Timestamps and Extract, CURRENT DATE & TIME, EXTRACT
- AGE, TO_CHAR, Mathematical Functions and Operators,CEIL & FLOOR, POWER, RANDOM
- ROUND, SETSEED, Operators and their precedence
Chapter 18: Basics and CRUD Operation
- Databases,Collection & Documents
- Shell & MongoDB drivers,What is JSON Data
- Create, Read, Update, Delete, Working with Arrays,Understanding Schemas and Relations
Chapter 19: Mongo DB
- What is MongoDB?,Characteristics, Structure and Features,MongoDB Ecosystem
- Installation process,Connecting to MongoDB database,What are Object Ids in MongoDB
- Data Formats in MongoDB,MongoDB Aggregation Framework,Aggregating Documents
- What are MongoDB Drivers?,Finding, Deleting, Updating, Inserting Elements
Chapter 20: Introduction To Tableau
- What is TABLEAU?,Why use TABLEAU?,Installation of TABLEAU
- Connecting to the data source,Navigating Tableau,Creating Calculated Fields
- Adding Colours,Adding Labels and Formatting,Exporting Your Worksheet
- Creating dashboard pages,Dashboard Tricks,Hands-on exercises
Chapter 21: Data Types in Tableau
- Pre-attentive processing,Length and position,Reference Lines
- Parameters,Tooltips,Data over time,Implementation,Advance table calculations
- Creating multiple joins in Tableau, Relationships vs Joins,Calculated Fields vs Table calculations
- Creating advanced table calculations,Saving a Quick table calculation,Writing your own Table calculations
- Adding a second layer moving average,Trendlines for power-insights
Chapter 22: Mapping and Analytics
- Getting started with visual analytics,Geospatial data
- Mapping workspace,Map layers, Custom territories,Common mapping issues
- Creating a map, working with hierarchies,Coordinate points,Plotting latitude and longitude
- Custom geocoding,Polygon Maps,WMS and Background,Image Creating a Scatter Plot, Applying Filters to Multiple Worksheets
Chapter 23: Calculations
- Aggregation and its types,level of detail common calculation functions
- creating parameters
Chapter 24: Dashboard and Stories
- Tiled vs Floating, Working in views with Dashboard and stories
- Legends, Quick filters
Chapter 25: Introduction To Power BI
- Why Power BI? Account Types,Installing Power BI
- Understanding the Power BI Desktop Workflow, Exploring the Interface of the Data Model
- Understanding the Query Editor Interface
Chapter 26: Query Editor
- Connecting Power BI Desktop to Source Files, Keeping & Removing Rows,Removing Empty Rows,Create calculate columns
- Make the first row as headers,Change Data type,Rearrange the columns,Remove duplicates, Unpivot columns and split columns
- Working with filters,Appending queries,Working with columns,Replacing values
- Splitting columns,Formatting data & handling formatting errors
Chapter 27: Power BI
- What is Power BI?,Working with Time series Understanding aggregation and granularity
- Filters and Slicers in Power BI,Maps, Scatterplots and BI Reports Creating a Customer Segmentation
Chapter 28: Data Models
- Understanding Relationships,Many-to-One & One-to-One,Cross Filter Direction & Many-to-Many
- M-Language vs DAX (Data Analysis Expressions), Basics of DAX,DAX Data Types,DAX Operators and Syntax
- Importing Data for DAX Learning, Resources for DAX Learning,M vS DAX,Understanding IF & RELATED, Create a Column
- Rules to Create Measures,Calculated Columns vs Calculated Measures,Understanding CALCULATE & FILTER,Understanding Data Category
- SUM, AVERAGE, MIN, MAX, SUMX, COUNT, DIVIDE, COUNT, COUNTROOMS, CALCULATE, FILTER, ALL,Time Intelligence
- Create date table in M,Create date table in DAX,Display last refresh date,SAMEPERIODLASTYEAR, TOTALYTD,DATEADD,PREVIOUSMONTH
Chapter -29 : time intelligence
- Create data table in M and DAX, Display last refresh Date
Chapter - 30 : Modelling
- Create your first report,Modelling basics to advance
- Modelling and relationship,Ways of creating the relationship
- Normalisation - De-,OLTP vs OLAP,Star schema vs Snowflake schema