This course is for anyone who is interested in learning Machine Learning.

Student should have some basic knowledge in Programming like If Conditions, Loops, etc., can opt this course.

This course contains topics like Numpy, Pandas, Timeseries Analysis, Matplotlib, Seaborn, ML Basics, Projects on Regression, Classification and Clustering, Model Tuning, Advanced Projects atleast 10 (Demo, Guided and Assignment based Projects)

Detail Topic List:

Introduction

• Course Overview
• Installation of Anaconda
• Jupyter Notebook Basics
• DataSets

 

Python Programming

• Operators
  • Arithmetic Operators
  • Comparison or Relational Operators
  • Logical or Boolean Operators
  • Bitwise Operators
  • Assignment Operators
  • Special Operators
• Math Library
• Variables
• Data Types
• Typecasting
• Booleans
• Strings
• Special Characters in a String
• Split and Strip a String
• Introduction to Lists
• Lists Slicing and Reverse Order
• Kinds of Lists
• Concatenate Strings Using join() method
• Add Lists
• Introduction to Dictionary
• Dictionary and Itâ??s Methods
• Nested Dictionary
• Create Dictionary Using zip() method
• Tuples
• Set
• If Condition
• While Loop
• Range() Method
• For Loop
• Reserve Keywords
• Built-In Functions
• User Defined Functions
• Anonymous or Lambda Functions
• File IO Operations

 

       

Numpy

• Necessity of Numpy
• Creation & Metadata of Numpy Arrays
• Broadcasting
• Numpy Built-In Functions
• Data Types
• Typecasting
• Matrix Multiplication
• Change of Numpy Shape
• Numpy Slicing
• Boolean Indexing
• Filter Data
• Statistical Methods
• Sort, Min & Max of Numpy Arrays
• Stacking & Splitting
• Copy Vs. View

 

 

Pandas

• Series
• DataFrame
• Metadata
• Rename Columns & Indices
• Transpose DataFrame
• Slice a DataFrame
• Boolean Indexing
• Missing Values
• Replace Values
• Search, Extract & Create New Columns
• Set & Unset Index
• Built-In Customized Functions
• Value_counts() Method
• Groupby() & Associated Methods
• Concat & Append
• Merge
• Reshape â?? Stack & Unstack
• Pivoting
• Melt
• Dummy Variables
• Crosstab() Method
• Upper, extract, replace & split Methods
• Regular Expressions
• Contains Method
• StartsWith Method
• Multiple String Method at a Time
• Manipulate Column Names
• Show Columns based on Keyword
• Read_csv() method
• Tabbed File
• Fixed Width Files
• JSON Data
• HTML Data
• XML Data
• API
• Export DataFrame to CSV File
• Encoded Data Files
• Bad Data
• Select Columns Based on Datatype

 

Time Series Analysis

• How to Convert Non-Timestamp To Timestamp
• Invalid Data
• Unix/Epoch Time
• Datetime Index
• Current Date Time
• Date_range & bdate_range Methods
• Pandas Slicing
• More components of Datetime
• Strftime() method
• Period Range
• Period
• Reseample
• Handle TimeZone

 

Matplotlib

• One Axis Plot
• Two Axis Plot
• Line Style & Color
• X and Y Limits
• Line Width
• Multiple Plots in One Chart
• Title, X & Y Labels
• Gridlines
• Annotations
• Ticks
• Spines
• Legend
• Subplots
• Line Plot
• Bar Graph
• Scatter Plot
• Area Plot
• Box Plot
• Histogram
• Pie Chart

       

Seaborn

• Count Plot
• Box Plot
• Violin Plot
• Swarm Plot
• Overlaying Plot of Univariate Variables
• Facet Grid
• Lmplot & regplot
• Size & Shape of a Plot
• Pair Plot
• Join Plot
• Heat Map

 

Statistics

• Types of Data
• Population Vs Sample
• Sampling Methods
• Branches of Statistics
• Distribution
• Variance Vs. Standard Deviation
• Z-Score
• Correlation
• Models
• Probability

 

Machine Learning Basics

• Labelled Vs Unlabeled Data
• Types of ML Algorithms
• How ML Predict things
• Count Vectorizer
• Difference between fit and fit_transform Methods
• Special & Numerical Chracters
• Remove HTML Tags from Text Data
• Remove Stop words from Text
• Stemming
• Train Test Split
• Accuracy â?? MAE, MSE, RMSE & Variance Score

Projects

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Classification Algorithms
• Clustering Algorithms

 

Model Tuning

• Alpha
• L1 Ratio
• N Estimators
• Max Features
• Learning Rate
• Max Depth
• C
• Kernel
• Gamma
• Criterion
• Splitter
• Random State
• Min Samples Split
• Max Iterations
• Dual
• Min Samples Leaf
• P
• N Neighbors
• Metric
• Alpha
• Fit Prior
• Priors

 

Projects

• Demo Projects â?? 3 to 4 Projects on Regression & Classification
• Guided Projects â?? 2 to 3 Projects
• Assignment Projects â?? 5 Projects