Certs Learning’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
Machine Learning Certification Training using Python
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Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
After completing this Machine Learning Certification Training using Python, you should be able to:
- Gain insight into the ‘Roles’ played by a Machine Learning Engineer
- Automate data analysis using python
- Describe Machine Learning
- Work with real-time data
- Learn tools and techniques for predictive modeling
- Discuss Machine Learning algorithms and their implementation
- Validate Machine Learning algorithms
- Explain Time Series and it’s related concepts
- Gain expertise to handle business in future, living the present
Certs Learning’s Python Machine Learning Certification Course is a good fit for the below professionals:
- Developers aspiring to be a ‘Machine Learning Engineer’
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- ‘Python’ professionals who want to design automatic predictive models
The pre-requisites for the Machine Learning Certification Training using Python includes development experience with Python. Fundamentals of Data Analysis practised over any of the data analysis tools like SAS/R will be a plus. However, Python would be more advantageous. You will be provided with complimentary “Python Statistics for Data Science Course” as a self-paced course once you enrol for the course.
1
Introduction to Data Science
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
2
Data Extraction, Wrangling,
Goal: Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.
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:
- 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 dataset in Python
- Arranging the data
- Plotting the graphs
3
Introduction to Machine Learning with Python
Goal: In this module, you will learn the concept of Machine Learning and it’s types.
Objective: 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:
- 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
4
Supervised Learning – I
Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Objective: 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:
- 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
5
Dimensionality Reduction
Goal: In this module you will learn about impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.
Objective: 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:
- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA
Hands On:
- PCA
- Scaling
6
Supervised Learning – II
Goal: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Objective: 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:
- 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
7
Unsupervised Learning
Goal: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
Objective: 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:
- 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
8
Association Rules Mining and Recommendation Systems
Goal: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.
Objective: 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:
- 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
9
Reinforcement Learning
Goal: 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.
Objective: 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:
- 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
10
Time Series Analysis
Goal: 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.
Objective: 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:
- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & 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
11
Model Selection and Boosting
Goal: 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 on how to convert weaker algorithms to stronger ones.
Objective: 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:
- 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
12
In-Class Project
Goal: In this module, you will learn how to approach and implement a Project end to end, and a Subject Matter Expert will share his experience and insights from the industry to help you kickstart your career in this domain. Finally, we will be having a Q&A and doubt clearing session.
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
You will never miss a lecture at CertsLearning! 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.
View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
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 a class.
All the instructors at Certs Learning are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by Certs Learning for providing an awesome learning experience to the participants.
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