Data Science

Mastering Machine Learning with Python  

Program Overview:

The term "Machine Learning" has become highly popular in today's technology, and it is expanding very quickly. Every day, without even recognizing it, we make use of Machine Learning. Some of the most trending real-world applications of Machine Learning include image and speech recognition, traffic prediction, product recommendations, email spam and malware filtering, fraud detection, stock market trading, and medical diagnosis
The main objective of this training program is to help participants gain the most recent practical skills that Machine Learning professionals utilize in their everyday jobs. They will acquire the knowledge and abilities required to use Python, which is swiftly moving to the top of the list of programming languages used by leading organizations globally, to develop data-driven Machine Learning models to resolve real-world applications.

Program Objective:

By the end of this course, the trainee will be able to:
  1. Understand the key concepts and tools in the field of Machine Learning and determine where they might be used effectively.
  2. Recognize the fundamental ideas of programming.
  3. Establish hands-on proficiency in Python programming.
  4. Acquire Python skills to manage, wrangle, and manipulate data.
  5. Implement popular data analysis techniques using Python.
  6. Develop supervised and unsupervised Machine Learning models using Python.
  7. Apply them to resolve real-world practical problems in a capstone project.

Program Module:

Upon completion of all the following modules, participants will receive the Professional Certificate in Machine Learning with Python.

 

1. Programming Fundamentals in Python

In this part, participants will learn about programming fundamentals, in general. Then, they will get introduced to the basics of Python programming, how to distinguish between string and numerical variables, how to build conditional statements and logical comparisons, and how to use functions as well as while and for loops. Parts of this module are as follows.
1.1. Programming Fundamentals.
1.2. Algorithms and Flowcharts.
1.3. Python Installation and Usage.
1.4. Variables and Expressions.
1.5. Data Structures in Python.
1.6. Working with Python Libraries.
1.7. Conditional Statements.
1.8. Python Functions.
1.9. Working with Loops and Iterations.

 

2. Fundamentals of Data Analysis in Python

In this module, participants will learn how to access, manipulate, and manage data using Python. In particular, they will carry out Data Wrangling and Feature Engineering using Python libraries, such as Numpy and Pandas. Further, they will learn the fundamentals of data analysis, as well as how to use Python to conduct inferential analysis, implement hypothesis testing, and test for associations. Following are all the points to be discussed in this module.
2.1. Fundamentals of Pandas and Numpy.
2.2. Data Wrangling and Manipulation.
2.3. Feature Engineering.
2.4. Data Visualization in Python.
2.5. Descriptive Statistics.
2.6. Exploratory Data Analysis.
2.7. Probability Distributions.
2.8. Hypothesis Testing.
2.9. T-Test, Chi-Squared Test, ana ANOVA.

 

3. Standard Algorithms of Machine Learning

Through this module, participants will get introduced to the key principles of Machine Learning processes and standard algorithms. Also, they will make use of Python Scikit-Learn to develop accurate classification and predictive models. Moreover, they are going to explore the fundamental concepts of unsupervised learning so that they can learn how to extract knowledge from unlabeled datasets. The module will cover exactly the following points.
3.1. Introduction to Machine Learning.
3.2. Fundamentals of Scikit-Learn as Python library for Machine Learning.
3.3. Supervised Learning.
3.3.1. Regression Models.
3.3.2. Evaluation Metrics.
3.3.3. Cross-Validation and Parameter Tuning.
3.3.4. Class-Imbalance Problem and Python Piplines.
3.3.5. K-Nearest Neighbours.
3.3.6. Decision Trees.
3.3.7. Naïve Bayes.
3.3.8. Support Vector Machines.
3.4. Unsupervised Learning.
3.4.1. K-Means.
3.4.2. Hierarchical Clustering.
3.4.3. Evaluation of Clustering Algorithms.

 

4. Ensemble Learning and Hybrid Models

By the end of this module, participants will acquire knowledge about the core concepts underlying hybrid models and ensemble learning, as well as how to apply the main ensemble techniques using Python Scikit-Learn. Parts of this module are as follows. At the end of this module, participants will learn how to conduct a capstone project to guarantee that they have a greater understanding of machine learning principles and methods by analyzing and visualizing data to resolve such real-world problems.
4.1. Ensemble Learning and Hybrid Models.
4.1.1. Bagging.
4.1.2. Random Forest.
4.1.3. Boosting.
4.1.4. Stacking.
4.2. Capstone project.
4.2.1. How to conduct a capstone project in Machine Learning.
4.2.2. Project Follow-up and Discussions.
4.2.3. Final Presentation and Assessment.

 

Program Duration:

100 hrs / 14 weeks

Assessments:

  • Classroom exercises.
  • Group assignments.
  • Individual assignments.
  • Capstone project.

Certification:

After successfully completing all four of the aforementioned modules, participants will be awarded a professional certificate in Machine Learning with Python.

Intended Learners:

A professional wishing to boost his/her profile with highly sought-after abilities, a business owner seeking to acquire an advantage in the marketplace, or someone looking to start a career in data science would all benefit from taking this diploma.

Prerequisites:

  • Basic computer skills.
  • An ability to install software from online websites.

 

For registration click here

Data Analysis with Python  

Program Overview:

Data analysis is the process of employing data to obtain pertinent information that could be used to inform decisions. It goes through several steps, such as building a data set, getting the data ready for processing, adopting models, figuring out the most important results, and producing reports. Participants in this program will learn the basics of statistics and how to apply them to a variety of real-world problems. They will be able to swiftly analyze their data and present the results using Python once they have finished this program.

Program Objective:

By the end of this course, the trainee will be able to:
  1. Understand the key concepts and tools of statistical data analysis.
  2. Make use of statistical tools for working with data sets.
  3. Establish hands-on proficiency in Python programming.
  4. Acquire Python skills to manage, wrangle, and manipulate data.
  5. Implement popular data analysis techniques using Python

Program Module:

Upon completion of all the following modules, participants will receive a certificate in Data Analysis with Python.

 

1. Programming Fundamentals in Python

The essential goals of this module are to teach participants the basics of Python programming, how to distinguish between string and numerical variables, how to build conditional statements and logical comparisons, and how to use functions as well as while and for loops. Parts of this module are as follows.
1.1 Programming Fundamentals.
1.2 Python Installation and Usage.
1.3 Variables and Expressions.
1.4 Data Structures in Python.
1.5 Working with Python Libraries.
1.6 Conditional Statements.
1.7 Python Functions.
1.8 Working with Loops and Iterations.

 

2. Exploratory Data Analysis in Python

In this module, participants will learn how to access, manipulate, and manage data using Python. In particular, they will carry out Data Wrangling and Feature Engineering using Python libraries, such as Numpy and Pandas. They will also learn how to design customized visualizations and generate a variety of plots in Python, such as scatter plots, count plots, bar plots, and box plots. By the end of this module, participants will feel confident performing their own exploratory data analysis in Python. They will be able to graphically present their findings to others and recommend the following actions for extracting further insights from the data.
2.1 Fundamentals of Pandas and Numpy.
2.2 Data Wrangling and Manipulation.
2.3 Feature Engineering.
2.4 Data Visualization in Python.
2.5 Cross-Tabulation.
2.6 Pivot Tables and Pivot Charts.
2.7 Relationships in Data.

 

3. Hypothesis Testing and Statistical Modelling in Python

By working with a verity of real datasets, participants will build and improve their analytical Python abilities as they explore when and how to apply popular statistical tests, such as t-test and chi-squared test. They will develop a thorough knowledge of how these tests work as well as the fundamental premises they are based on. Participants will get introduced to the key principles of standard predictive algorithms in Statistics. They will utilize Python Scipy to develop accurate statistical models. The module will cover exactly the following. At the end of this module, they will learn how to conduct a capstone project to make sure that they have a better understanding of statistical analysis principles and methods by analyzing and visualizing data in context of real-world problems.
3.1 Hypothesis Testing and Statistical Modelling
3.1.1 Introduction to Hypothesis Testing.
3.1.2 Parametric Tests.
3.1.3 Non-Parametric Tests.
3.1.4 Linear Regression.
3.1.5 Logistic Regression.
3.1.6 Evaluation Metrics.
3.2 Capstone Project
3.2.1 How to conduct a capstone project in Data Analysis.
3.2.2 Final Presentation and Assessment.

 

4. Ensemble Learning and Hybrid Models

By the end of this module, participants will acquire knowledge about the core concepts underlying hybrid models and ensemble learning, as well as how to apply the main ensemble techniques using Python Scikit-Learn. Parts of this module are as follows. At the end of this module, participants will learn how to conduct a capstone project to guarantee that they have a greater understanding of machine learning principles and methods by analyzing and visualizing data to resolve such real-world problems.
4.1. Ensemble Learning and Hybrid Models.
4.1.1. Bagging.
4.1.2. Random Forest.
4.1.3. Boosting.
4.1.4. Stacking.
4.2. Capstone project.
4.2.1. How to conduct a capstone project in Machine Learning.
4.2.2. Project Follow-up and Discussions.
4.2.3. Final Presentation and Assessment.

 

Program Duration:

60 hrs

Assessments:

  • Classroom exercises.
  • Group assignments.
  • Individual assignments.
  • Capstone project.

Certification:

After successfully completing all of the aforementioned modules, participants will be awarded a professional certificate in Data Analysis with Python.

Intended Learners:

A program for data analysis is designed for people who wish to learn Python completely from scratch as well as get started in the field of Statistical Data Analysis.

Prerequisites:

  • Basic computer skills.
  • An ability to install software from online websites.
  • Prior awareness of Excel sheets.

 

For registration click here

 

 

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