CertAchiever

Machine Learning Certification Training using Python

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Instructor-led live online classes

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Machine Learning Certification Training using Python

$399

Machine Learning Certification Training using Python

CertAchiever Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.

Training Features

Instructor-led Sessions

Both Weekdays and Weekend batches are available.

Real-Life Case Studies

During the sessions you will be working on the case studies provided by the trainer.

Lifetime Access

You will get the life time access of training material including videos & study guides..

Assignments

Each class will be followed by practical assignments to practice the concepts covered in class

24 x 7 Expert Support

We have a lifetime 24x7 online support team to resolve all your technical queries,through a ticket based tracking system..

Certification

Towards the end of the course, you will be awarded with Training competition certification based on you performance.

Course Curriculum

Learning 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 Covered

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

Learning Objectives

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

Topics Covered

Data Analysis Pipeline, What is Data Extraction, Types of Data, Raw and Processed Data, Data Wrangling, Exploratory Data Analysis, Visualization of Data

Hands On

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

Learning Objectives

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, hyper parameters, loss function and optimization.

Topics Covered

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.

Hands On

Linear Regression - Using Boston dataset

Learning Objectives

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

Topics Covered

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?

Hands On

Implementation of Logistic regression, Decision tree, Random forest

Learning Objectives

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

Topics Covered

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

Hands On

Learning Objectives

At the end of this module, you should be able to: Understand What is Naïve Bayes Classifier How Naïve Bayes Classifier works? Understand Support Vector Machine Illustrate How Support Vector Machine works? Hyperparameter optimization

Topics Covered

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

Learning Objectives

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

Topics Covered

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?

Hands On

Implementing K-means Clustering, Implementing Hierarchical Clustering

Learning Objectives

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

Topics Covered

What are Association Rules?, Association Rule Parameters, Calculating Association Rule Parameters, Recommendation Engines, How Recommendation Engines work?, Collaborative Filtering, Content Based Filtering

Hands On

Apriori Algorithm, Market Basket Analysis

Learning Objectives

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

Topics Covered

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

Hands On

Calculating Reward, Discounted Reward, Calculating Optimal quantities, Implementing Q Learning, Setting up an Optimal Action

Learning Objectives

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

Topics Covered

What is Time Series Analysis?, Importance of TSA, Components of TSA, White Noise, AR model, MA model, ARMA model, ARIMA model, Stationarity, ACF & PACFChecking Stationarity, Checking Stationarity, Converting a non-stationary data to stationary, Implementing Dickey Fuller Test, Plot ACF and PACF

Hands On

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

Learning Objectives

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

Topics Covered

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

Hands On

Cross Validation, AdaBoost

Learning Objectives

At the end of this module, you should be able to: How to approach a project Hands-On project implementation What Industry expects Industry insights for the Machine Learning domain QA and Doubt Clearing Session

Topics Covered

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

FAQ's

You will never miss a lecture at CertAchiever! You can choose either of the two options:

View the recorded session of the class available in your LMS.

·You can attend the missed session, in any other live batch."

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.

You can give us a CALL at +918882136965 (IN) OR email at admin@certachiever.com

Certification

Once your training will complete, you will be awarded with training completion certification from the Trainer.