Machine Learning with Python
✓ Supervised & Unsupervised Learning, Regression & Classifications
✓ Advanced Machine Learning algorithms such as Decision Trees, SVM and Clustering
✓ Data Validations
✓ Build and deploy deep learning and data visualization models and apply the same in real world projects
Machine Learning with Python
Duration: 5 days/40 hours
Why Machine Learning:
In this course, we will learn about how Data Science and Machine Learning are used as well as the benefits and risks of implementing these solutions. As we all know Machine learning falls under the ambit of artificial intelligence. With Machine Learning , one would understand how software and application functionality could turn into a more robust and efficient product with the incorporation of algorithms that could predict outcomes. In this Machine Learning with Python course, students will begin their learning journey with the basics/ fundamentals of machine learning using python and eventually move towards data exploration, explore to various machine learning approaches such as supervised and unsupervised learning, regression, and classifications and more. Data visualisation and exploration can be done using Python and its libraries such as Pandas, Matplotlib and Scikit, which would be one of the core knowledge to be acquired in this Python for Machine Learning course.
40 hours of Instructor-Led Machine Learning with Python Training
10 hours of Python Fundamentals self paced learning
Trained by highly qualified and experienced trainers
Project and hands-on learning structure.
Certification of Completion that demonstrates your skills to companies for career advancement purposes.
Ability to build various python programs including distribution, user-defined functions, and import datasets in python environment and more
Data manipulation and analysation using Pandas library
Data Visualisation with Python Libraries such as : Matplotlib, Seaborn and ggplot
Build data distribution models
Perform Hypothesis Testing to calculate conditional probability
Performing analysis of variance (ANOVA)
Build linear regression models, evaluating model parameters and measuring performance metrics
Build logical regression models, evaluating model parameters and measuring performance metrics
Ability to perform K-means clustering and hierarchical clustering
Ability to build KNN algorithm models with the intention to find the optimum value of K
Building decision tree models for both regression and classification problems
Using ensemble techniques like averaging, weighted averaging and max voting
Utilise bootstrap sampling, bagging and boosting techniques
Build random forest models
Identify the optimum number of components/factors by utilising scree plot and one-eigenvalue criterion
Ability to perform PCA/Factor Analysis
Build recommendation engines
There are no prerequisites to learn this course. Prior knowledge of foundational python programming is recommended.
Machine Learning Engineers
Professionals looking to gain a thorough understanding of the Machine Learning with Python
To have the skill sets that are in demand now which distinguishes you from your peers which ultimately allows employers to give you an upper hand in the selection process.
Having the Machine Learning with Python certification , not only it validates your skills , but ability to kickstart career with higher starting salaries, better job benefits, better opportunities for career advancement and more versatile responsibilities in comparison to their non- certified peers.
The average salary of Machine Learning with Python certified professionals is in the range of USD 77,000 to USD 152,000 per annum.
Understanding statistical concepts for machine learning
Understand the applicability of Python for Machine Learning
Understand Supervised Learning
Understand Unsupervised Learning
Course Fee: RM 3000
Duration: 5 Days/40 Hours
The inquiry process comprises three simple steps.
Submit Inquiry- Tell us a bit about yourself and the questions you want to enquire
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Physical Classroom Training (Malaysia)
On-site Company Training (Malaysia)
Online Training via Microsoft Team (Malaysia and International)
Module 1 – Introduction To Data Science And Data Science Libraries
Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses. Through this module, you will learn the basics, how to analyze data, and then create some beautiful visualizations using Python.
It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.
Pandas is an important library in Python for Data Science. It is used for data manipulation and analysis. It is well suited for different data such as tabular, ordered and unordered time series, matrix data, etc.
Matplotlib is a powerful library for visualization in Python. It can be used in Python scripts, shell, web application servers, and other GUI toolkits. Because of the graphs and plots that it produces, it’s extensively used for data visualization.
Seaborn is a statistical plotting library in Python. So, whenever you’re using Python for Data Science, you will be using matplotlib (for 2D visualizations) and Seaborn, which has its beautiful default styles and a high-level interface to draw statistical graphics.
Next in the list of the top python libraries for data science comes Scikit-learn, a machine learning library that provides almost all the machine learning algorithms you might need. Scikit-learn is designed to be interpolated into NumPy and SciPy.
• Exploratory Data Analysis
Exploratory data analysis (EDA) is an especially important activity in the routine of a data analyst or scientist. It enables an in depth understanding of the dataset, define or discard hypotheses and create predictive models on a solid basis. It uses data manipulation techniques and several statistical tools to describe and understand the relationship between variables and how these can impact business.
Exploratory Data Analysis (EDA) – Credit Card Fraud Detection Case Study
Overview : Lots of financial losses are caused every year due to credit card fraud transactions, the financial industry has switched from a posterior investigation approach to an a priori predictive approach with the design of fraud detection algorithms to warn and help fraud investigators.
This case study is focused to give you an idea of applying Exploratory Data Analysis (EDA) in a real business scenario. In this case study, apart from applying the various Exploratory Data Analysis (EDA) techniques, you will also develop a basic understanding of risk analytics and understand how data can be utilized in order to minimize the risk of losing money while lending to customers.
Module 2 – Introduction To Machine Learning
This machine learning module gives you an introduction to machine learning along with the wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, train- validation-test splitting, and various sequential models. This module is a stepping stone to your Machine Learning journey.
• Introduction To Machine Learning and Categories
Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed. At a broad level, machine learning can be classified into three types - Supervised learning, Unsupervised learning and Reinforcement learning.
• Your First Machine Learning Model
You will use the scikit-learn library to create your models. When coding, this library is written as sklearn, as you will see in the sample code. Scikit-learn is easily the most popular library for modeling the types of data typically stored in DataFrames.
Case Study : How Experience Affects Salary
Overview : We will learn how to build a simple linear regression model on given dataset, we will be using salary dataset. Our dataset will have 2 columns namely – Years of Experience and Salary. Main objective here is to learn steps to building and using a model. We will be predicting salaries based on experience. We will learn Splitting the dataset and fitting linear regression model into the training set.
Module 3 – Credit Card Fraud Detection with Python & Machine Learning
For any bank or financial organization, credit card fraud detection is of utmost importance. We have to spot potential fraud so that consumers cannot bill for goods that they haven’t purchased. The aim is, therefore, to create a classifier that indicates whether a requested transaction is a fraud. In this machine learning project, we solve the problem of detecting credit card fraud transactions using machine numpy, scikit learn, and few other python libraries. We overcome the problem by creating a binary classifier and experimenting with various machine learning techniques to see which fits better.
• Perform Exploratory Data Analysis (EDA) on our dataset
Here we will use data manipulation techniques and several statistical tools to describe and
understand the relationship between variables and how these can impact business.
• Apply different Machine Learning algorithms to our dataset
Let's train different models on our dataset and observe which algorithm works better for our problem. This is actually a binary classification problem as we have to predict only 1 of the 2 class labels. We can apply a variety of algorithms for this problem like Random Forest, Decision Tree, Support Vector Machine algorithms, etc.
In this machine learning project, we build Random Forest and Decision Tree classifiers and see which one works best.
• Train and Evaluate our models on the dataset and pick the best one
Now, Let’s train and evaluate the newly created models on the dataset and pick the best one. Train the decision tree and random forest models on the dataset using the fit() function. Record the predictions made by the models using the predict() function and evaluate.
• Class Imbalance Issue
It is the problem in machine learning where the total number of a class of data (positive) is far less than the total number of another class of data (negative). This problem is extremely common in practice and can be observed in various disciplines including fraud detection, anomaly detection, medical diagnosis, oil spillage detection, facial recognition, etc.
Module 4 – Hyperparameters and Model Validation
Cross-validation is a statistical method used to estimate the performance of machine learning models. It is a method for assessing how the results of a statistical analysis will generalize to an independent data set.
• Hold Out Cross-validation
• Leave One Out Cross Validation (LOOCV)
• K-Fold Cross-Validation
• Monte Carlo Cross-Validation(Shuffle Split)
• Hyperparameter Tuning Machine Learning
Choosing the correct hyperparameters for machine learning or deep learning models is one of the best ways to extract the last juice out of your models.
Module 5 – Credit Risk Modelling in Python
Credit risk is the risk of a borrower not repaying a loan, credit card or any other type of credit facility. Credit risk is an important topic in the field of finance because banks and other financial institutions heavily invest in reducing their credit risk. The main reason behind the global financial crisis in 2008 was that mortgage loans were given to customers with poor credit scores. Poor credit score indicates that a customer has a higher probability of defaulting a loan.
Machine Learning models have helped these companies in improving the accuracy of their credit risk analysis by giving a scientific technique for predicting potential debtors.
The objective of this article is to build a model to predict probability of person defaulting a loan. The following steps will be followed in building the model.
• Data Preprocessing and data preparation
• Features Engineering and Features selection
• Model Development and Model Evaluations
• Building Model
Logistic Regression Random Forest Classifier XGBoost Classifier
Support vector machine classifier
• Evaluating the model
SMOTE (Synthetic Minority Oversampling Technique)
Module 6 – Bank Churn Prediction using popular classification algorithms
Knowing the customer churn rate is a key indicator for any business. According to a study by Bain & Company, improving the customer retention rate for existing customers by just 5 percent can improve a company’s profitability by 25 to 95 percent.
In this module, we are going to look at the following:
• Initial Exploratory Data Analysis
• Predicting the churn rate for a customer and classify them by learning about different classification algorithms.
• Comparing and evaluating different algorithms based on its performance.
• And once we have our best model, we would perform optimization
Overview : This module uses customer data from a bank to build a predictive model for the likely churn clients. As we know, it is much more expensive to sign in a new client than to keep an existing one. It is advantageous for banks to know what leads clients to leave the company. Churn prevention allows companies to develop loyalty programs and retention campaigns to keep as many customers as possible.
Module 7 – Unsupervised Machine Learning
In the previous topics, we learned supervised machine learning in which models are trained using labeled data under the supervision of training data. But there may be many cases in which we do not have labeled data and need to find the hidden patterns from the given dataset. So, to solve such types of cases in maSchine learning, we need unsupervised learning techniques.
• Apriori algorithm
Apriori algorithm refers to the algorithm which is used to calculate the association rules between objects. It means how two or more objects are related to one another. In other words, we can say that the apriori algorithm is an association rule leaning that analyzes that people who bought product A also bought product B.
• Components of Apriori algorithm Support , Confidence And Lift
• Implementing Apriori algorithm in Python
Apriori Algorithm is a Machine Learning algorithm which is used to gain insight into the structured relationships between different items involved. The most prominent practical application of the algorithm is to recommend products based on the products already present in the user’s cart. Walmart especially has made great use of the algorithm in suggesting products to it’s users.
Machine Learning with Python Exam Format
While there is no examination linked directly to this course, there are a number of examinations available on the market for students to validate their machine learning with python abilities.
Salary Demand of certified Machine Learning with Python qualification:
The average salary of Machine Learning with Python certified professionals is in the range of USD 77,000 to USD 152,000 per annum.
There is a direct examination for Machine Learning with Python Course. You can however, demonstrate your Python skills by taking up the entry-level Python examination by Python Institute , upon passing you will be certified as Certified Entry Level Python Programmer.
Certified Entry Level Python Programmer certification can be a great way to demonstrate your skills with the python language and distinguish you from your peers.
PCEP™: Exam Information
SPECIFICATIONS ITEM DESCRIPTION
Exam name:- PCEP™ – Certified Entry-Level Python Programmer
Exam Code & Current Exam Versions:- PCEP-30-02 (Status: Active) & PCEP-30-01 (Status: Retiring – December 31, 2022)
Exam Duration: PCEP-30-02 – Exam: 40 minutes, NDA/Tutorial: 5 minutes
PCEP-30-01 – Exam: 45 minutes, NDA/Tutorial: 5 minutes
Number of Questions: 30
Format:- Single- and multiple-select questions, drag & drop, gap fill, sort, code fill, code insertion | Python 3.x
Passing Score:- 70%
Languages:- English, Spanish
Cost:- USD 59 (Exam: Single-Shot)
USD 76.70 (Exam: with one retake)
USD 71.00 (Exam: Single-Shot + Practice Test)
USD 29 (Practice Test)
Delivery Channel:- OpenEDG Testing Ser