CERTIFICATION COURSE

No. of Hours: 50

Certificate Validity: 2 Yr(s)

Introduction

This is an integrated program in Data Science and Machine Learning primarily designed for professionals pursuing career as Data Scientist, AI Engineers, Data Analyst and ML Engineers.

Our customized Data Science and Machine Learning Courses are designed by Data Scientists who are working on ML projects in our company. This program will help you achieve proficiency in Python programming language, data analysis, data pre-processing, machine learning algorithms, classification systems, regression modeling, clustering algorithms, recommender systems, and natural language processing to work on real world projects and case studies. These skills will help you prepare for the role of the Data Scientist.

Why to enroll for this course?

There is a huge requirement of Data Scientists in almost all domains including IT, Automobile, Telecommunication, Construction etc. and this is a major concern for Top MNCs organizations around the world. The major corporations are ready to pay top salaries for professionals with the good Data Science skills. This Data Science and Machine Learning Course equips with all the latest technologies Python, Data Analytics, and NLP. Thus you can easily take your career to the next level after completion of this certification course.

Prerequisite for learning

There are no special prerequisites required for Data Science and Machine Learning training. However, the knowledge of the following will make it easier:

  • Basic Mathematics
  • Simple Statistics
  • Knowledge of programming language
  • Business domain knowledge

Who will be our trainers ?

You can check the company and trainers profile here.

Course Contents

Introduction to Python
– Overview
– Basic Syntax
– Interpreter and Editor
– Keywords & Identifiers
– Comments
– Input methods
– Assigning values
– Data types

Python Type Conversion, Decisions and Loops
– Type Conversion
– Decision making
— a) If..
— b) If..else
— c) Nested if..else
— d) If..elif..else
– Loops
— a) For loop
— b) While loop
— c) Nested loops
— d) break, pass and continue

Python Handling Data-types
– String
— a) Creating and using
— b) Methods and operations
– List
— a) Creating and using
— b) Methods and operations
– Tuple
— a) Creating and using
— b) Methods and operations
– Dictionary
— a) Creating and using
— b) Methods and operations

Python Procedures
– Function
– Parameter passing
– Types of Parameter passing
– Managing return types
– Default paremeters
– Recursion
– Variable arguments
– lambda, map and filters

Python Object Orientation
– Classes
– Methods
– Importing Class
– Access specifiers
– Special Parameters
– Constructor

Python File I/O Operations
– Opening & Closing files
– File Object Attributes
– Modes of File
– Reading, Writing and Appending files
– Renaming & Deleting files
– seek and tell

Python Exception Handling
– Handling using Exception class
– The try clause
– The except clause
– Except clause with No Exception
– Except clause with Multi-Exception
– try-finally block
– try-except-else-finally
– Argument of Exception
– User Defined Exception

Python Package Operations
– What is Package, Library, Module?
– Installing Official Packages
– Installing Third-party Packages
– Installing using pip, apt and setup
– Importing Packages as a whole
– Importing selected module from Packages
– Alias naming packages / modules / libraries
– Creating your own module and package

Python IDE
– IDE Selection
– Intro to Spyder, IDLE and Pycharm
– Setting up IDE (Pycharm)
– Execution methods in an IDE
– Selective run and view

Introduction to Machine Learning
– What is Machine Learning?
– Data Mining and Data Analytics
– Uses and Abuses
– How do machine learn?
– Training the data
– Generalization
– Steps of Machine Learning
– Types of Machine Learning
– Matching data to Machine Learning

Pre-requisites of Machine Learning
– Introduction to numpy
– Creating the ndarray
– Introduction to matplotlib.pyplot
– Plotting line graphs, bar charts
– Histograms
– Getting the Datasets
– Why csv files?
– Introduction to Pandas
– Reading Datasets
– Dataframe & Series
– Accessing rows and columns
– Accessing values in data cell
– DataFrame operations
– Operating on NaN values
– Creating filters
– Exporting Datasets

Machine Learning Initiation
– Data Pre-processing intuition
– Importing Libraries
– Importing Dataset
– Handling Missing Data
– Categorical Data
– Training and Testing data
– Feature Scaling or Standardization
– Normalization
– Binarization
– Label encoding
– One hot encoding

Machine Learning Algorithms

Supervised Learning

– Linear Regression
— Simple example
— Importing Datasets (Experience)
— Data preprocessing
— Training and Testing split
— Characterizing regression
— Find coefficients and intercepts
— Visualization
— Applications

– Multi Linear Regression
— Importing Datasets (Car data)
— Data preprocessing
— Feature scaling
— Training and Testing split
— Characterizing regression
— Find coefficients and intercepts
— Finding accuracy

– Polynomial Regression
— Theoretical introduction
— Polynomial curves
— Polynomial Features
— Importing Datasets (Purchase)
— Data pre-processing
— Characterizing regression
— Finding accuracy
— Visualization

– Logistic Regression
— Theoretical introduction
— Importing Datasets (Purchase)
— Data pre-processing
— Feature scaling
— Characterizing regression
— Finding accuracy
— Confusion Matrix

– Decision Tree Classification
— Theoretical introduction
— Examples
— Scatterplot
— Decision Tree model
— Strengths and weakness
— Pruning
— Gini and entropy
— Simplified Decision Tree Ex.
— Importing Datasets (Banknotes)
— Train the classifiers
— Train and test split
— Characterizing classifier
— Confusion matrix
— Visualizing the tree

– Decision Tree Regression
— Importing Datasets (Petrol)
— Preparing data
— Training the regressor
— Characterizing regressor
— Accuracy score

– Random Forest Regression
— Ensemble learning
— How Random Forest works?
— Advantages and Disadvantages
— Preparing data
— Training the regressor
— Evaluating regressor
— Accuracy score

– Random Forest Classification
— Importing Datasets (Banknotes)
— Train and test split
— Train the classfiers
— Feature scaling
— Characterizing classifier
— Confusion matrix

– K-Nearest Neighbor
— What is KNN algorithm?
— Pros and cons
— Importing Dataset & Libraries
— Preprocessing
— Train and test split
— Feature scaling
— Training and predictions
— Evaluating the algorithm
— Comparing error rate- k value

– Support Vector Classification
— What is Support Vector?
— Simple SVM
— Sample Example
— Importing Dataset & Libraries
— Preprocessing
— Train and test split
— Feature scaling
— Training and predictions
— Evaluating the algorithm
— Kernel SVM
— Polynomial Kernel
— Gaussian Kernel
— Sigmoid Kernel
— Comparing Kernel performance

– Support Vector Regression
— Importing Datasets
— Preparing data
— Training the regressor
— Characterizing regressor
— Accuracy score

Unsupervised Learning

– KMeans Clustering
— Unsupervised learning flow
— What is clustering?
— K-means clustering
— Generalized algorithm
— Limitations and use cases
— Importing datasets (Customers)
— Finding the clusters (Elbow method)
— The kmeans() function & attributes
— Visualize the elbow
— Finding the clusters
— Centroids
— Visualize the clusters

– Hierarchical Clustering
— Clustering steps
— How it works?
— Dendrogram
— Measures of distance
— Linkage Criteria
— Agglomerative vs. Divisive
— Sample program
— Checking the clusters
— Importing datasets (Customers)
— Using dendrogram and linkage
— Apply the clustering
— Visualizing clusters

– Association Rule Mining
— Association rules
— Market Basket Analysis
— Applications
— Conceptualizing associations
— Apriori algorithm
— Support, confidence and lift
— Leverage and conviction
— Structured and non-structured transactions
— Import dataset (Retail)
— Transaction encoder
— Getting association rules
— Conditional rules
— Operation on rules

Reinforcement Learning
— Formulating a problem
— Comparison with other ML methods
— Framework for solving RL problems
— Implementation
— Increasing the complexity

Natural Language Processing
— Introduction to nltk
— Text preprocessing
— Tokenizing
— Weighted frequency
— replace, summarization
— sorting
— Example: Wikipedia
— Sentence scores

Introduction to Neural Networks
— What is Neural network?
— Perceptron
— Artificial Neural Network
— Importing Dataset & Libraries
— Preprocessing
— Train and test split
— Feature scaling
— Training and predictions
— Evaluating the algorithm

What next? Deep Learning

Configuration:
1. Operating System: Ubuntu 18.04 LTS
2. RAM: 4 to 6 GB
3. Processor: i3 (Minimum)

Timings:
6 Hrs per Week (Saturday and Sunday)
2 Months

Weekly Assignment:
1. After every weekend session you’ll need to solve assignments until next week.
2. On completion of 1 module, you are required to appear exam.
3. All exams will be taken either on Center / Off site. When Off-site you will submit assignments via mail.

Final Exam:
A duration of 7 days, will be given after completion of Course
The Final exam will be a conducted once, in case of Failure; exam can be reappeared after another 4 months

Opportunities:
As first Opportunity after Passing exams, you will deployed on a Well monitored Live Project.
You are required to resolve the Problem statement with Expected outputs, with that you will be receiving a token of Completion Letter of respective Project.