Welcome to digiStackedu
Courses Images
10 SEATS

COURSE INSTRUCTOR

Teachers Images

Ashok Mahajan

Sr. Data Analyst

Mr. Ashok Mahajan is one of the best instructor of digiStackedu.

BASIC INFORMATION

  • Price : Call Now/-
  • Lessons : 17
  • Length : 3 Month
  • Level : Advance
  • Category : Analysis
  • Started : 25 Aug 2022
  • Shift : 02
  • Class : 90

Course Description

Machine learning is an emerging field in the current industry, professionals and students are looking for their carer in this field. In this course, you will learn python programming, statistics, and machine learning algorithms Moreover, this subject is practical oriented, explore analytical methods, recognizing complex patterns, and focuses on how a program automatically learns and improves with its experience, and makes intelligent decisions by analyzing different types of data.

This is one of the best course to be a data analyst, you will get a top-level instructor and work on real-time projects with IT experts, take your first step on the road to success.

We will start this course with the basics of python programming and further, we will move to the python advanced level libraries that are required to do data analysis like NumPy, Pandas, and Matplotlib apart from this, you will learn how to analyze different types of data sets and how to visualize and generate reports.

Course Syllabus

Module 1: Python programming Overview

  1. Introduction to python.
  2. Setting python environment.
  3. installation of anaconda.
  4. Variables,data type and operators in python.
  5. Type casting in Python.
  6. Taking User input Python.
  7. Basic Problem solving skills in python.
  8. Solving Mathmetical equations in Python.
  1. Introduction to Conditional Statements.
  2. Understanding Basics of If and Else.
  3. Controlling the flow of program using IF and Else.
  4. User Input with If-Else
  5. Nesting of If-Else Staements.
  6. Practical Demo on If-Else.
  7. Assignments on If-Else Lecture.
  8. IF and Else with Loop.
  9. Nesting of IF and Else Statements.
  1. Introduction to Loop in Python programming
  2. Basics of for Loop and While Loop.
  3. Combining Loop and If-Else Statements.
  4. Positive and Negative Loop.
  5. Practical on For Loop in Python programming.
  6. Practical on Nested For Loop in Python programming.
  7. Practical on While Loop in Python programming.
  8. Practical on Nested While Loop in Python programming.
  9. For and while Loop on R List.
  10. For and while Loop on R Tuples.
  11. Switch Case Statement in Python Programming.
  1. Introduction to List in Python
  2. List with Loop.
  3. List inside If and Else Statement.
  4. List Append and Extend Function.
  5. Delete Element from Python List.
  6. Update Python List Elements.
  7. Count() function on List.
  8. In and Not in Operators on List.
  9. List in reverse order
  10. Shorting Python List.
  11. Assignments on Python List.
  1. An Introduction to Tuples.
  2. Concept of immutability.
  3. Count Operation on Tuples.
  4. Arithmetic Operators on Tuples.
  5. Checking if two tuples are equal.
  6. Which Operations are Not Allowed on Tuples.
  7. Assignments on Tuples.
  8. Handling duplicates using Python Sets.
  9. Working on Set Constructor.
  1. An Introduction to Dictionaries in Python.
  2. Data Structure of a Dictionary.
  3. adding and removing key value pairs.
  4. updating key value pairs inside a dictionary
  5. Creating dynamic Dictioanries.
  6. Dynamic Dictionary Using functions.
  7. assignments on Python Dictionaries.
  1. An Introduction to Functions in Python.
  2. Creating a First Function.
  3. Function with parameters.
  4. Functions with Return type
  5. Function with Python List and Tuples.
  6. Functions with Python Loop.
  7. Functions for Python Dictionaries.
  8. Python lambda Function.
  9. Assignment on Python Function.
  1. An Introduction to Class in Python.
  2. An Introduction to Objects in Python.
  3. Creating First Class and Object.
  4. Constructors in Python
  5. Self Keyword in Python Object Oriented Programming.
  6. Inheritance in Python.
  7. Overloading and Overriding in Python.
  8. Assignment on Python Class and Object.
  1. An Introduction to File I/O in Python.
  2. Reading from a file.
  3. Writing into a file.
  4. Reading a doc file in python
  5. Reading pdf in Python.
  6. Opening and Closing Files.
  7. File I/O Modes in Python.
  8. Assignment on Python I/O.
  1. Introduction to Numpy.
  2. Working on NumPy Array.
  3. Arithmetic Operators on Numpy Array.
  4. Inspecting NumPy Array.
  5. Subsetting, Slicing, Indexing on Numpy.
  6. Manipulation on Numpy Array.
  7. Stacking and Splitting Numpy Array.
  8. Aggregate Functions on Numpy Array.
  9. Copying and Sorting Numpy Array.
  10. Introduction to Pandas
  11. Creating DataFrames using Pandas
  12. Reshaping Data – Changing the layout of a data set
  13. Handling Missing values using Pandas
  14. Combining Data Sets using Pandas
  15. Grouping Data uisng Pandas and Pivot Table.
  16. Applying functions on Pandas Data Frame
  17. Plotting Time Series using Pandas
  1. Introduction to Matplotlib
  2. Matplotlib basic plotting and containers.
  3. Working on size of figures and subplots.
  4. Histograms and bar chart using Matplotlib.
  5. Time Series chart using Matplotlib.
  6. Working on Correlation chart or heatmap.
  7. Stacking and Splitting Numpy Array.
  8. Setting up Plot Title,Legend and Axes levels.
  9. Copying and Sorting Numpy Array.
  10. Linear Regression line using Matplotlib.
  11. Creating DataFrames using Pandas
  12. Joining Matplotlib and Seaborn.
  1. Introduction to Statistics
  2. Arithmetic Mean,Median and Mode.
  3. Variance and standard deviation using Excel.
  4. Discriptive Stats using Excel.
  5. Probability distribution Concept.
  6. Continuous Vs Discrete probability distribution.
  7. Understanding normal distribution Central limit theorem.
  8. Inferential Statistics-An Introduction to Hypothesis testing.
  9. Z and t Test using python.
  10. ANOVA and CHI square Test Examples.
  11. Pivot Table Concept in Excel.
  12. Correlation and Co-Variance Concept.
  13. Introduction to Box Plot and its Python Implementation.
  1. An Introduction to Machine Learning.
  2. Supervised vs unsupervised Machine Learning.
  3. Data Preprocessing and cleaning techniques.
  4. Regression techniques - Linear,multiple and polynomial regression.
  5. Machine learning model cost functions.
  6. An Introduction to classification.
  7. Underfitting and overfitting in Machine Learning.
  8. Understanding Confusion Matrix and Roc Curve.
  9. Building First Classification Model.
  10. Logistic Regression and its deep Background.
  1. Building Decision Tree Model.
  2. Ensemble Learning Techniques.
  3. Understanding Random Forest.
  4. Understanding gradient boosted trees.
  5. Naive Bayes for classification.
  6. KNN - K Nearest Neighbour.
  7. Support Vector Machine and its background.
  8. Advance Regression technique-Ridge and Lasso
  1. An Introduction to Clustering.
  2. K-Mean Clustering Machine Learning.
  3. Hierarchical clustering in machine learning.
  4. Agglomerative hierarchical clustering in python
  5. Calculating cost of a cluster.
  6. Finding the Best Value of K.
  7. Understanding dendrogram for Cluster Visualization.
  8. Linkage algorithm in clustering
  1. An Introduction to Project Development.
  2. Project Assignments.
  3. Task Allocation and Project guidance.
  4. Final Project Submission.

Student Reviews

Our Student reviews after completion of this course.

Frequently Asked Questions


Yes, you will get industry valid certificate after completeion of this course and you will be tagged as a Data Analyst.
Yes, You can pay your fee in two installments but it depands on the course you selected.
Yes, You can join Machine Learning online course even if you do not belong from computer science background. Our experts will help you to upgarde your technical skills, you just need some basic computer skills for this course.
This course is 100% practical oriented, you will work on Python, Machine Learning, Statistics, Numpy and Pandas moreover, you will learn how to use Data Science toolkit for building Machine learning Models.
DigiStackEdu provide cost effective and quality training,We focus on every student and we understand the value of money.Our Trainers are certified and having more than 10+ years of experience in Digital Marketing, Data Science, Java Programming, Web Designing, Big Data and Data Analytics moreover,We have Trained 47,000+ students and professionals who are working in top level IT companies.
DigiStackEdu only provide internship in Data Science, Digital Marketing, Java Programming, PHP programming and Web Development.For more details please contact to our support team.
Machine Learning is the most demanding field in the current IT industry and you'll get a lot of job opportunity after completion of this course as Data is tremendously growing at companies server so there is a need of data experts who can manage and analyze this huge volume of data.
You can submit your fee after 3 classes, Even after that if you face any issue with in the next 7 days then you can contect to our support team for the return.
We'll start this course by very basic concepts of python programming further we'll move to the advance concept of Data Science like Statistics, NumPY, Pandas, Machine Learning .It will take approx 3 month to be master of Machine Learning after that you will get a industry valid cerficate and will get a tag as a Data Analyst.