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Machine Learning with Python-2 Training & Certification Course


Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of Computer Programs that can change when exposed to new data. In this article, we’ll see the basics of Machine Learning and the implementation of a simple machine learning algorithm using python.

Machine learning is one of the hottest new technologies to emerge in the last decade, transforming fields from consumer electronics and healthcare to retail. This has led to intense curiosity about the industry among many students and working professionals. Python is a general-purpose high-level programming language that is being increasingly used in data science and in designing machine learning algorithms. This tutorial provides a quick introduction to Python and its libraries like numpy, scipy, pandas, matplotlib and explains how it can be applied to develop machine learning algorithms that solve real-world problems.


The Reader must-have basics of Artificial Intelligence

Full Description

 Introduction to Data Science


Get an introduction to Data Science in this Module and see how Data Science helps to analyze large and unstructured data with different tools.


At the end of this module, you should be able to:

• Define Data Science

• Discuss the era of Data Science

• Describe the role of a Data Scientist

• Illustrate the Life cycle of Data Science

• List the Tools used in Data Science

• State what role Big Data and Hadoop, Python, R and Machine Learning play in Data Science


• What is Data Science?

• What does Data Science involve?

• Era of Data Science

• Business Intelligence vs Data Science

• The life cycle of Data Science

• Tools of Data Science • Introduction to Python

Data Extraction, Wrangling, & Visualization


Discuss the different sources available to extract data, arrange the data in a structured form, analyze the data, and represent the data in a graphical format.


At the end of this module, you should be able to:

• Discuss Data Acquisition techniques

• List the different types of Data

• Evaluate Input Data

• Explain the Data Wrangling techniques

• Discuss Data Exploration


• Data Analysis Pipeline

• What is Data Extraction

• Types of Data

• Raw and Processed Data

• Data Wrangling

• Exploratory Data Analysis

• Visualization of Data Hands-On/Demo:

• Loading different types of the dataset in Python

• Arranging the data

• Plotting the graphs

Introduction to Machine Learning with Python


In this module, you will learn the concept of Machine Learning and it’s types.


At the end of this module, you should be able to:

• Essential Python Revision

• Necessary Machine Learning Python libraries

• Define Machine Learning

• Discuss Machine Learning Use cases

• List the categories of Machine Learning

• Illustrate Supervised Learning Algorithms

• Identify and recognize machine learning algorithms around us

• Understand the various elements of machine learning algorithm like parameters, hyperparameters, loss function and optimization.


• Python Revision (numpy, Pandas, scikit learn, matplotlib)

• What is Machine Learning?

• Machine Learning Use-Cases

• Machine Learning Process Flow

• Machine Learning Categories

• Linear regression

• Gradient descent


• Linear Regression – Using Boston Dataset

Supervised Learning - I


In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.


At the end of this module, you should be able to:

• Understand What is Supervised Learning?

• Illustrate Logistic Regression

• Define Classification

• Explain different types of Classifiers such as Decision Tree and Random Forest


• What is Classification and its use cases?

• What is Decision Tree?

• Algorithm for Decision Tree Induction

• Creating a Perfect Decision Tree

• Confusion Matrix

• What is the Random Forest?


• Implementation of Logistic regression, Decision tree, Random forest

Dimensionality Reduction


In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing the LDA model.


At the end of this module, you should be able to:

• Define the importance of Dimensions

• Explore PCA and its implementation

• Discuss LDA and its implementation


• Introduction to Dimensionality

• Why Dimensionality Reduction


• Factor Analysis

• Scaling dimensional model




Scaling Supervised Learning - II


In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.


At the end of this module, you should be able to:

• Understand What is Naïve Bayes Classifier

• How Naïve Bayes Classifier works?

• Understand the Support Vector Machine

• Illustrate How Support Vector Machine works?

• Hyperparameter optimization


• What is Naïve Bayes?

• How Naïve Bayes works?

• Implementing Naïve Bayes Classifier

• What is Support Vector Machine?

• Illustrate how Support Vector Machine works?

• Hyperparameter optimization

• Grid Search vs Random Search

• Implementation of Support Vector Machine for Classification Hands-On:

• Implementation of Naïve Bayes, SVM

Unsupervised Learning


In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.


At the end of this module, you should be able to:

• Define Unsupervised Learning

• Discuss the following Cluster Analysis o K - means Clustering o C - means Clustering o Hierarchical Clustering


• What is Clustering & its Use Cases?

• What is K-means Clustering?

• How does K-means algorithm work?

• How to do optimal clustering

• What is C-means Clustering?

• What is Hierarchical Clustering?

• How Hierarchical Clustering works? Hands-On:

• Implementing K-means Clustering

• Implementing Hierarchical Clustering

Association Rules Mining and Recommendation Systems


In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.


At the end of this module, you should be able to:

• Define Association Rules

• Learn the backend of recommendation engines and develop your own using python


• What are Association Rules?

• Association Rule Parameters

• Calculating Association Rule Parameters

• Recommendation Engines

• How Do Recommendation Engines work?

• Collaborative Filtering

• Content-Based Filtering


• Apriori Algorithm

• Market Basket Analysis

Reinforcement Learning


In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent-based on agent environment interaction.


At the end of this module, you should be able to

• Explain the concept of Reinforcement Learning

• Generalize a problem using Reinforcement Learning

• Explain Markov’s Decision Process

• Demonstrate Q Learning


• What is Reinforcement Learning

• Why Reinforcement Learning

• Elements of Reinforcement Learning

• Exploration vs Exploitation dilemma

• Epsilon Greedy Algorithm

• Markov Decision Process (MDP)

• Q values and V values

• Q – Learning

• α values


• Calculating Reward

• Discounted Reward

• Calculating Optimal quantities

• Implementing Q Learning

• Setting up an Optimal Action

Time Series Analysis


In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modelling such that you analyse a real time-dependent data for forecasting.


At the end of this module, you should be able to:

• Explain Time Series Analysis (TSA)

• Discuss the need of TSA

• Describe ARIMA modelling

• Forecast the time series model


• What is Time Series Analysis?

• Importance of TSA

• Components of TSA

• White Noise

• AR model

• MA model

• ARMA model

• ARIMA model

• Stationarity



• Checking Stationarity

• Converting a non-stationary data to stationary

• Implementing Dickey-Fuller Test

• Plot ACF and PACF

• Generating the ARIMA plot

• TSA Forecasting

Model Selection and Boosting


In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn how to convert weaker algorithms to stronger ones.


At the end of this module, you should be able to:

• Discuss Model Selection

• Define Boosting

• Express the need of Boosting

• Explain the working of Boosting algorithm


• What is the Model Selection?

• The need for Model Selection

• Cross-Validation

• What is Boosting?

• How Boosting Algorithms work?

• Types of Boosting Algorithms

• Adaptive Boosting


• Cross Validation

• AdaBoost 

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