**Title**

Machine Learning Certification Training Using Python is a stepping stone for a new journey in a field of computer science. This course includes learning about python ecosystem, methods of ML, data loading, data with statistics and visualization, data feature selection, ML algorithms based on Classification, Regression, KNN and Clustering.

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StepLeaf’s Machine Learning Certification Training using Python Course helps you to extract meaningful raw data to solve complex problems quickly. The key focus of this course is to understand and implement various ML algorithms based on Classification, Regression, KNN and Clustering. The algorithms include Logistic Regression, SVM, Decision Trees, Naive Bayes, Random Forest, Linear Regression, K-Means, Mean Shift, Hierarchical Clustering, Performance Metrics, Automatic Workflow and improve performance of ML models

**Course Objective
**

Mastering a technology is an art of talking with machines. At the end of the course, you will be mastered in the following topics:

- Understand the Roles played by ML Engineer
- ML application in real-world scenario use cases
- Understand and implement ML algorithms
- Data loading in ML applications
- Understand data feature selection
- Using Python automate data analysis
- Use of tools and techniques in ML models
- Understand time series in detail
- Identify trends and patterns in a business

**Who should take up this Certification Course?
**

StepLeaf’s Machine Learning Training Course is mainly preferred for Analytics Manager, Software Developers, Business Analytics, Integration specialists, Information Architects and Python Professionals.

**What are the prerequisites for this course?
**

Little knowledge in the following topics will explode your learning into a masterpiece

- Python coding
- Fundamentals of Data Analysis.

Python, bigdata, hadoop, machinelearning, randomforest, decisiontree, naïvebayes, datascience, dataextraction, dataanalysispipeline, matrix, pca, lda, gridsearch, randomsearch, k-meansclustering, reinforcementlearning, plotacf, pacf, tsaforecasting

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

**Objectives:** 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

**Topics:
**

- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
- Introduction to Python

- 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

- Loading different types of dataset in Python
- Arranging the data
- Plotting the graphs

- 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, hyper parameters, 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

**Hands-On: **

- Linear Regression – Using Boston Dataset

- 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 Random Forest?

- Implementation of Logistic regression, Decision tree, Random forest

- Define the importance of Dimensions
- Explore PCA and its implementation
- Discuss LDA and its implementation

- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA

**Hands-On:
**

- PCA
- Scaling

- Understand What is Naïve Bayes Classifier
- How Naïve Bayes Classifier works?
- Understand 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

- Implementation of Naïve Bayes, SVM

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 K-means algorithm works?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How Hierarchical Clustering works?

- Implementing K-means Clustering
- Implementing Hierarchical Clustering

- 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 Recommendation Engines work?
- Collaborative Filtering
- Content Based Filtering

- Apriori Algorithm
- Market Basket Analysis

- Explain the concept of Reinforcement Learning
- Generalize a problem using Reinforcement Learning
- Explain Markov’s Decision Process
- Demonstrate Q Learning

**Topics: **

- 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

- 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
- ACF & PACF

- Checking Stationarity
- Converting a non-stationary data to stationary
- Implementing Dickey Fuller Test
- Plot ACF and PACF
- Generating the ARIMA plot
- TSA Forecasting

- Discuss Model Selection
- Define Boosting
- Express the need of Boosting
- Explain the working of Boosting algorithm

- What is Model Selection?
- Need of Model Selection
- Cross – Validation
- What is Boosting?
- How Boosting Algorithms work?
- Types of Boosting Algorithms
- Adaptive Boosting

- Cross Validation
- AdaBoost

- How to approach a project
- Hands-On project implementation
- What Industry expects
- Industry insights for the Machine Learning domain
- QA and Doubt Clearing Session

Structure your learning and get a certificate to prove it.

**What are the system requirements for our Machine Learning Certification Training using Python? **

The practical training is done in a Cloud Lab environment. This environment already has the required software in it.

**
How will I execute my practicals? **

The Case Studies are executed using Jupyter Notebook in Cloud Lab. The necessary instruction will be given by our StepLeaf instructor to execute all the assignments.

**
What are the Case studies for this course? **

There are totally 40 case studies as a part of this training. Given below are few of them.

**
Case Study 1 **

An Online Hotel booking application wants to create a recommendation of optimal suggestions for the users to book a hotel. Predict the hotel cluster for the user to book a room in a hotel using multi-class classification problems, build SVM and decision tree.

**Case Study 2 **

In an Online Public Library users are requested to search for their individual choice of book. Using the ML model suggests that users read some more books based on his past purchase and refer to similar books read by other users. Help the library to find the error in their approach and build a profitable application.

**
Case Study 3 **

Do an end-to-end case study using time series analysis and forecasting with ML using Python. Extract meaningful statistics and find the insight of the data to predict future value with observed values.

**
Case Study 4 **

A construction company had a problem with its clients based on the quality of the building being constructed. Do an analysis and figure out all the different department in constructing a building and discover the problem and efficiency in each department which hinders the quality. Implement a proper solution to the problem

**
What are the projects for this course? **

There are totally 5 projects as a part of this training. Given below is one of them.

**Description **

**
**1. Download and install Python SciPy

2. Load dataset

3. Create 6 Machine Learning models, find the best with accuracy