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Machine Learning training is designed to help you develop necessary skills that are vital to gain in-depth knowledge in supervised learning, unsupervised learning, machine learning algorithms, vector machines, and much more through real-time use cases and project-based learning. If you are an aspirant who wants to explore how a device can recognize patterns and messages from data, then machine learning with Python is an ideal start for you! We’ll prepare you to play with the army of powerful machine learning models to solve any problem.

Enroll NowThe Machine Learning training by Codegnan has over 60 hours to ensure that you have the proper understanding of every concept before going to the next module. You will get acquainted with the requirement of product-based companies. The detailed overview on algorithms and techniques, such as regression, classification, time series modeling, supervised and unsupervised learning, SKLearn usage, statistical thinking, etc. Working on real-time case studies will make you understand the job aspect of a Machine Learning engineer. We assure you the great command in practicals along with theoretical concepts. So, get leveled up in Machine Learning in less investment! Your dream job is waiting for you!

50 Hours Instructor Led Training

Self-Paced Videos

Exercises & Projects

Certification

Flexible Schedule

Lifetime Access & Upgrade

24/7 Lifetime Support

Machine Learning Introduction

1. Importance of Data in 21st Century

2. Types of Data and its usage

3. Python Crash course (using IDLE)

● Data Types

● Conditional Statements

● Control Statements

● Functions

4. What is Machine Learning and types of Learning

5. Anaconda Environment Setup and its usage .

2. Types of Data and its usage

3. Python Crash course (using IDLE)

● Data Types

● Conditional Statements

● Control Statements

● Functions

4. What is Machine Learning and types of Learning

5. Anaconda Environment Setup and its usage .

Python Machine Learning Libraries

1. Numpy

● Creating arrays, Difference between List and Array

● Accessing Elements, Slicing, Concatenation

● Universal Functions, Shape Manipulation

● Automatic Reshaping, Vector Stacking.

2. Pandas

● Pandas DataStructures

● Indexing, Selecting Data, Slicing functions

● Some Useful DataFrame Functions

● Handling Missing Values in DataFrame

● Time Series Analysis

3. Data Visualization Libraries

● Matplotlib

● Plotly

● Basic Plotting with Seaborn

● Projects on Data Analysis – Google Analysis, Market Analysis

4. Sklearn usage

● Playing with Scikit-learn, Understanding classes in Scikit-learn

● Creating arrays, Difference between List and Array

● Accessing Elements, Slicing, Concatenation

● Universal Functions, Shape Manipulation

● Automatic Reshaping, Vector Stacking.

2. Pandas

● Pandas DataStructures

● Indexing, Selecting Data, Slicing functions

● Some Useful DataFrame Functions

● Handling Missing Values in DataFrame

● Time Series Analysis

3. Data Visualization Libraries

● Matplotlib

● Plotly

● Basic Plotting with Seaborn

● Projects on Data Analysis – Google Analysis, Market Analysis

4. Sklearn usage

● Playing with Scikit-learn, Understanding classes in Scikit-learn

Machine Learning Fundamentals

Machine Learning Fundamentals

• What is machine learning?

• How Machine Learning works?

• Applications of machine learning

• Different types of machine learning

• How do we know machines are learning right?

• Different stages of machine learning projects.Data Transformation and Preprocessing

• Handling Numeric Features

• Feature Scaling

• Standardization and Normalization

• Handling Categorical Features

• One Hot Encoding, pandas get_dummies

• Label Encoding

• More on different encoding techniquesTrain,Test and Validation Split

• Simple Train and Test Split

• Drawbacks of train and test split

• K-fold cross validation

• Time based splittingOverfitting And Underfitting

• What is overfitting ?

• What causes overfitting?

• What is Underfitting ?

• What causes underfitting ?

• What are bias and Variance ?

• How to overcome overfitting and underfitting problems ?

• What is machine learning?

• How Machine Learning works?

• Applications of machine learning

• Different types of machine learning

• How do we know machines are learning right?

• Different stages of machine learning projects.Data Transformation and Preprocessing

• Handling Numeric Features

• Feature Scaling

• Standardization and Normalization

• Handling Categorical Features

• One Hot Encoding, pandas get_dummies

• Label Encoding

• More on different encoding techniquesTrain,Test and Validation Split

• Simple Train and Test Split

• Drawbacks of train and test split

• K-fold cross validation

• Time based splittingOverfitting And Underfitting

• What is overfitting ?

• What causes overfitting?

• What is Underfitting ?

• What causes underfitting ?

• What are bias and Variance ?

• How to overcome overfitting and underfitting problems ?

Supervised Machine Learning Algorithms

Regression

• Introduction to Linear Regression

• Understanding How Linear Regression Works

• Maths behind Linear Regression

• Ordinary Least Square

• Gradient Descent

• R - square

• Adjusted R-square

• Polynomial Regression

• Multiple Regression

• Performance Measures - MSE, RMSE, MAE

• Assumption of Linear Regression

• Ridge and Lasso regression

• RFE (Recursive Feature elimination)

Hands On - Problem formulation and Case Study on Hotstar, Netflix, And housing prices Dataset

Classification

Logistic regression

• Introduction to classification problems

• Introduction to logistic regression

• Why the name regression ?

• The sigmoid function

• Log odds

• Cost function

• Feature importance and model interpretability

• Collinearity of features

• Feature engineering for non-linearly separable data

Performance Metrics for Classification Algorithms

• Accuracy Score

• Confusion Matrix

• TPR, FPR, FNR, TNR

• Precision - Recall

• F1-Score

• ROC Curve and AUC

• Log LossHands On - Real World Case Study on IBM HR Employee Attrition datasetK Nearest Neighbors• Introduction to KNN

• Effectiveness of KNN

• Distance Metrics

• Accuracy of KNN

• Effect of outlier on KNN

• Finding the k Value

• KNN on regression

• Where not to use KNN

Hands On - Different case study on KNN

Natural Language Processing

• Introduction to NLP

• Converting Text to vector

• Data Cleaning

• Preprocessing Text Data - Stop word removal, Stemming , Tokenization, Lemmatization

• Collecting Data from the web

• Developing a Classifier

• Building Pipelines for NLP projects

• Uni-grams,bi-grams and n-grams

• tf-idf

• Word2Vec

Hands On - Text Summarization, WebScraping for data, Sentiment Analysis, Topic Modelling, Text Summarization and Text Generation

Naive Bayes

• Refresher on conditional Probability

• Bayes Theorem

• Examples on Bayes theorem

• Exercise problems on Naive Bayes

• Naive Bayes Algorithm

• Assumptions of Naive Bayes Algorithm

• Laplace Smoothing

• Naive Bayes for Multiclass classification

• Handling numeric features using Naive Bayes

• Measuring performance of Naive Bayes

Hands On - Working on spam detection and Amazon Food Review dataset

Support Vector Machines

• Introduction to SVM

• What are hyperplanes ?

• Geometric intuition

• Maths behind svm’

• Loss Function

• Kernel trick

• Polynomial kernel, rbf and linear kernels

• SVM Regression

• Tuning the parameter

• GridSearch and RandomizedSearch

• SVM Regression

Hands On - Case Study SVM on Social network ADs and Gender recognition from voice datasetDecision Tree

• Introduction to Decision Tree

• Homogeneity and Entropy

• Gini Index

• Information Gain

• Advantages of Decision Tree

• Preventing Overfitting

• Advantages And Disadvantages

• Plotting Decision Trees

• Plotting feature importance

• Regression using Decision Trees

Hands-On - Decision Tree on US Adult income dataset

Ensemble Learning

• Introduction to Ensemble Learning

• Bagging (Bootstrap Aggregation)

• Constructing random forests

• Runtime

• Case study on Bagging

• Tuning hyperparameters of random forest(GridSearch, RandomizedSearch)

• Measuring model performance

• Boosting

• Gradient Boosting

• Adaboost and XGBoost

• Case study on boosting trees

• Hyperparameter tuning

• Evaluating performance

• Stacking Models

Hands-On - Talking Data Ad Tracking Fraud Detection case study

• Introduction to Linear Regression

• Understanding How Linear Regression Works

• Maths behind Linear Regression

• Ordinary Least Square

• Gradient Descent

• R - square

• Adjusted R-square

• Polynomial Regression

• Multiple Regression

• Performance Measures - MSE, RMSE, MAE

• Assumption of Linear Regression

• Ridge and Lasso regression

• RFE (Recursive Feature elimination)

Hands On - Problem formulation and Case Study on Hotstar, Netflix, And housing prices Dataset

Classification

Logistic regression

• Introduction to classification problems

• Introduction to logistic regression

• Why the name regression ?

• The sigmoid function

• Log odds

• Cost function

• Feature importance and model interpretability

• Collinearity of features

• Feature engineering for non-linearly separable data

Performance Metrics for Classification Algorithms

• Accuracy Score

• Confusion Matrix

• TPR, FPR, FNR, TNR

• Precision - Recall

• F1-Score

• ROC Curve and AUC

• Log LossHands On - Real World Case Study on IBM HR Employee Attrition datasetK Nearest Neighbors• Introduction to KNN

• Effectiveness of KNN

• Distance Metrics

• Accuracy of KNN

• Effect of outlier on KNN

• Finding the k Value

• KNN on regression

• Where not to use KNN

Hands On - Different case study on KNN

Natural Language Processing

• Introduction to NLP

• Converting Text to vector

• Data Cleaning

• Preprocessing Text Data - Stop word removal, Stemming , Tokenization, Lemmatization

• Collecting Data from the web

• Developing a Classifier

• Building Pipelines for NLP projects

• Uni-grams,bi-grams and n-grams

• tf-idf

• Word2Vec

Hands On - Text Summarization, WebScraping for data, Sentiment Analysis, Topic Modelling, Text Summarization and Text Generation

Naive Bayes

• Refresher on conditional Probability

• Bayes Theorem

• Examples on Bayes theorem

• Exercise problems on Naive Bayes

• Naive Bayes Algorithm

• Assumptions of Naive Bayes Algorithm

• Laplace Smoothing

• Naive Bayes for Multiclass classification

• Handling numeric features using Naive Bayes

• Measuring performance of Naive Bayes

Hands On - Working on spam detection and Amazon Food Review dataset

Support Vector Machines

• Introduction to SVM

• What are hyperplanes ?

• Geometric intuition

• Maths behind svm’

• Loss Function

• Kernel trick

• Polynomial kernel, rbf and linear kernels

• SVM Regression

• Tuning the parameter

• GridSearch and RandomizedSearch

• SVM Regression

Hands On - Case Study SVM on Social network ADs and Gender recognition from voice datasetDecision Tree

• Introduction to Decision Tree

• Homogeneity and Entropy

• Gini Index

• Information Gain

• Advantages of Decision Tree

• Preventing Overfitting

• Advantages And Disadvantages

• Plotting Decision Trees

• Plotting feature importance

• Regression using Decision Trees

Hands-On - Decision Tree on US Adult income dataset

Ensemble Learning

• Introduction to Ensemble Learning

• Bagging (Bootstrap Aggregation)

• Constructing random forests

• Runtime

• Case study on Bagging

• Tuning hyperparameters of random forest(GridSearch, RandomizedSearch)

• Measuring model performance

• Boosting

• Gradient Boosting

• Adaboost and XGBoost

• Case study on boosting trees

• Hyperparameter tuning

• Evaluating performance

• Stacking Models

Hands-On - Talking Data Ad Tracking Fraud Detection case study

Un-Supervised Machine Learning Algorithms

Clustering

• Introduction to unsupervised learning

• Applications of Unsupervised Learning

• Kmeans Geometric intuition

• Maths Behind Kmeans

• Kmeans in presence of outliers

• Kmeans random initialization problem

• Kmeans++

• Determining the right k

• Evaluation metrics for Kmeans

• Case study on Kmeans

• Hierarchical Clustering

• Agglomerative and Divisive

• Denodgrams

• Case study on hierarchical clustering

• Segmentation

• Case Study on Segmentation

• DBSCAN - Density based clustering

• MinPts and Eps

• Core Border and Noise Points

• Advantages and Limitation of DBSCAN

• Case Study on DBSCAN clustering

Hands On - Applying Unsupervised models on Retail data and mall customer datasetDimensionality Reduction Techniques

• What are dimensions?

• Why is high dimensionality a problem ?

• Introduction to MNIST dataset with (784 Dimensions)

• Into to Dimensionality reduction techniques

• PCA (Principal Component Analysis) for dimensionality reduction

• t-sne (t-distributed stochastic neighbor embeddingHands-on: Applying Dimensionality Reduction on MNIST data

• Introduction to unsupervised learning

• Applications of Unsupervised Learning

• Kmeans Geometric intuition

• Maths Behind Kmeans

• Kmeans in presence of outliers

• Kmeans random initialization problem

• Kmeans++

• Determining the right k

• Evaluation metrics for Kmeans

• Case study on Kmeans

• Hierarchical Clustering

• Agglomerative and Divisive

• Denodgrams

• Case study on hierarchical clustering

• Segmentation

• Case Study on Segmentation

• DBSCAN - Density based clustering

• MinPts and Eps

• Core Border and Noise Points

• Advantages and Limitation of DBSCAN

• Case Study on DBSCAN clustering

Hands On - Applying Unsupervised models on Retail data and mall customer datasetDimensionality Reduction Techniques

• What are dimensions?

• Why is high dimensionality a problem ?

• Introduction to MNIST dataset with (784 Dimensions)

• Into to Dimensionality reduction techniques

• PCA (Principal Component Analysis) for dimensionality reduction

• t-sne (t-distributed stochastic neighbor embeddingHands-on: Applying Dimensionality Reduction on MNIST data

REINFORCEMENT LEARNING

• Introduction

• Markov Decision Process

• Expected Return

• Policy and Value Function

• Q-Learning

• Exploration vs Exploitation

• OpenAI Gym and python for Q-learning

• Training Q-Learning Agent

• Watching Q-Learning Play GamesHands On - Working with OpenAI Gym and Q-Learning

• Markov Decision Process

• Expected Return

• Policy and Value Function

• Q-Learning

• Exploration vs Exploitation

• OpenAI Gym and python for Q-learning

• Training Q-Learning Agent

• Watching Q-Learning Play GamesHands On - Working with OpenAI Gym and Q-Learning

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Medical Treatment

Currently, the interpretation of genetic mutations is being made manually. This is a very time-consuming task where a clinical pathologist must manually review and classify every single genetic mutation based on evidence from text-based clinical literature. We will build a machine learning model to organize genetic variation on Kaggle personalized medicine dataset.

Stack Overflow

The problem says that we will be provided a bunch of questions. A question in Stack Overflow contains three segments Title, Description, and Tags. Using the text in the title and description, we should suggest the tags related to the subject of the question automatically.

Taxi Demand Prediction

We will predict the demand for taxi services in new york city. To find several pickups, given location coordinates (latitude and longitude) and time, in the query region and surrounding regions.

AD Click Prediction

we will work with a marketing agency’s advertising data to develop a machine learning algorithm that predicts if a particular user will click on an advertisement.

Quora Problem

On Quora, anyone can ask any question, and there are also chances that when you go to ask your question, someone might have already answered. We will classify the duplicate question pairs on the quora platform using machine learning technique

Netflix Movie Recommendation

Building a recommendation system that recommends the movies to the Netflix users based on their interests. We will use different approaches to build the recommendation system.

Machine Learning Certification

Training on different technologies provided by Codegnan is a set of blended learning models that brings classroom learning experience with its world-class LMS. We understand the effort of students; thus, as a token of motivation, our training is honored by top leading industries like Microsoft and HP. After the successful completion of your Machine Learning course, you will be awarded Codegnan’s certification.

Can I get a job with Machine Learning certification?

We can't even imagine what will happen with the technology we'll see in the coming years, but we know we already have a lack of skilled AI and machine-learning professionals. Only when we get people educated and put in the millions of AI jobs can the gap increase. If you want to be one of those experts, get certified the sooner you start your training you will be working in this exciting and rapidly changing field.

7 YEARS+ EXPERIENCE IN DATA SCIENCE, DATA ANALYSIS - CEO AND FOUNDER OF CODEGNAN IT SOLUTIONS

He is a tech-expert with 7 years of industrial experience in Python, Data Analysis, Big Data, Machine Learning and NLP. He has 360 degrees of expertise in all these subjects. He is known for his practical approach to different real-time industrial problems. He is known for his great interest in helping students reach their true potential and scale greater heights. Believing in Problem-based teaching pedagogies, he left his job in Malaysia as a data engineer and came back to the newly born state to fill the void between students and the industry.

OPERATIONS HEAD AND DATA SCIENCE TRAINER CODEGNAN IT SOLUTIONS (OPC) PVT LTD

A Master in Computational Intelligence blended with a passion for nurturing the meaning of education with technology. Engaging, understanding, and knowledgeable technical trainer with over 5 years of experience in educating students, seasoned employees and new hires in the software industry. Certified as an Azure AI Engineer and a Microsoft Certified Trainer. Also adept at teaching users a variety of software programs and technologies. Dynamic communicator with excellent presentation skills, able to translate complex concepts into understandable terms using creative teaching methods. Proven track record of more than 1500 students with industry jobs in emerging technologies. With expertise in Machine Learning, Data Analytics, Natural Language Processing, he believes in teaching all functionalities to the core making his life's motto to train students to be a Data Scientist rather than a Data Engineer.

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