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Machine Learning Training Course

Machine Learning is a branch of Artificial Intelligence that allows machines to learn and improve from experience automatically. It is defined as the field of study that gives computers the capability to learn without being explicitly programmed. It is quite different from traditional programming.

machine learning

familiarize

Familiarize yourself

  • You will learn how to use data science and machine learning with Python.
  • You will create data pipeline workflows to analyze, visualize, and gain insights from data.
  • You will build a portfolio of data science projects with real world data.
  • You will be able to analyze your own data sets and gain insights through data science.
  • Master critical data science skills.
  • Understand Machine Learning from top to bottom.
  • Understand the full product workflow for the machine learning lifecycle.

Key Features of Machine learning Course

Free Demo Session

Take a free demo session and self - experience the learning that takes place.

Project Based Training

We help you with the concepts of ML by hands-on ML through project based learning.

Industry Professional

Our expert ML faculties will guide you to establish a successful career in ML.

Practical Assignment

Each class will be followed by practical assignments which will aggregate to minimum 15 hours

Study Materials

Effective resources and notes will be made available easily on different topics.

Flexible Batches

If you miss any topic, you can attend the same in upcoming batches.

Tools Covered

Machine Learning Development Course Content

Introduction to Machine Learning (ML)

    What is Machine Learning ?
    Use Cases of Machine Learning.
    Types of Machine Learning – Supervised to Unsupervised methods.
    Machine Learning workflow.

Statistics for Data Science

    Common charts used.
    Inferential Statistics.
    Probability, Central Limit theorem, Normal Distribution & Hypothesis testing.

Data Visualization

    Plotting basic statistical charts in Python.
    Data visualization with Matplotlib.
    Statistical data visualization with Seaborn.
    Interactive data visualization with Bokeh.

Exploratory Data Analysis

    Introduction to Exploratory Data Analysis (EDA) steps.
    Plots to explore relationship between two variables.
    Histograms, Box plots to explore a single variable.
    Heat maps, Pair plots to explore correlations.

Data Preprocessing

    Preprocessing techniques like missing value imputation.
    Encoding categorical variables.
    Scaling, Too many nulls, Same values/skew.
    Data types, Missing value imputation.
    When column doesn’t have missing values.
    Categorical Attributes, Related Attributes.

Linear Regression

    Introduction to Linear Regression.
    Use cases of Linear Regression.
    How to fit a Linear Regression model?
    Evaluating and interpreting results from Linear Regression models.
    Project:
  • How linear regression helps determine demand in Restaurant.

Logistic Regression

    Introduction to Logistic Regression.
    Logistic Regression use cases.
    Understand use of odds & Logit function to perform logistic regression.
    Project:
  • Predicting default cases in the Banking Industry

Decision trees & Random Forests

    Introduction to Decision Trees & Random Forest
    Understanding criterion(Entropy & Information Gain) used in Decision Trees.
    Using Ensemble methods in Decision Trees.
    Applications of Random Forest.
    Project:
  • Predict passengers survival in a Ship mishap.

Model evaluation techniques

    Introduction to evaluation metrics and model selection in Machine Learning.
    Importance of Confusion matrix for predictions.
    Measures of model evaluation – Sensitivity, specificity, precision, recall & f-score.
    Use AUC-ROC curve to decide best model.
    K-fold Cross Validation.
    Parameter Tuning.
    Grid Search.
    XGBoost
    Project
  • Applying model evaluation techniques to prior projects.

Dimensionality Reduction using PCA

    Introduction to KNN.
    Calculate neighbours using distance measures.
    Find optimal value of K in KNN method.
    Advantage & disadvantages of KNN.
    Project:
  • Optimize model performance using PCA on high dimension dataset.

KNN (K – Nearest neighbours)

    Unsupervised Learning: Introduction to Curse of Dimensionality.
    What is dimensionality reduction?
    Technique used in PCA to reduce dimensions.
    Applications of Principle component Analysis (PCA).
    Project:
  • Classify smokers among
  • Classify malicious websites using close neighbour technique.
  • Credit scoring analysis using weighted k nearest neighbor

Naïve Bayes classifier

    Introduction to Naïve Bayes classification.
    Refresher on Probability theory.
    Applications of Naive Bayes Algorithm in Machine Learning.
    Project:
  • Classify Spam SMS, based on probability.

K-means clustering technique

    Introduction to K-means clustering.
    Decide clusters by adjusting centroids.
    Find optimal ‘k value’ in kmeans.
    Understand applications of clustering in Machine Learning.
    Project:
  • Predict flower species in Iris flower data.

Support vectormachines (SVM)

    Introduction to SVM.
    Figure decision boundaries using support vectors.
    Find optimal ‘k value’ in kmeans.
    Applications of SVM in Machine Learning.
    Project:
  • Predicting wine quality without tasting the wine.
  • Personal Info App.

Time series forecasting

    Introduction to Time Series analysis.
    Stationary vs non stationary data.
    Components of time series data.
    Interpreting autocorrelation & partial autocorrelation functions.
    Stationarize data and implement ARIMA model.
    Project:
  • Forecast demand for Air travel.

Ensemble learning

    Introduction to Ensemble Learning.
    What are Bagging and Boosting techniques?
    What is Bias variance trade off?
    Project:
  • Predict annual income classes from adult census data.
  • Personal Info App.

Stacking

    Introduction to stacking.
    Use Cases of stacking.
    How stacking improves machine learning models?
    Project:
  • Predict survivors in Titanic case

Optimization

    Introduction to optimization in ML.
    Applications of optimization methods.
    Optimization techniques: Linear Programming using Excel solver.
    How Stochastic Gradient Descent(SGD) Works?
    Project:
  • Apply SGD on Regression data (sklearn dataset).
  • Personal Info App.

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Certification

This is a ML Certificate Course which offers an added leverage to help you kickstart your career.

Placements

we provide PLACEMENT ASSISTANCE, and we are the no 1 Machine Learning classes in Ranchi.

Awards

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FAQ's

What is Machine learning?

Machine learning is the ability of a system to learn a task without being explicitly programmed from given data. It focuses on the development of computer programs that can access data and use it to learn for themselves.Machine learning (ML) is a subfield of an Artificial intelligence (AI).Tom Mitchell formally defined machine learning as “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”In the above definition, there are three key things- task (let’s say identification of human face from an image), performance measure (how accurately an algorithm identifies whether a human face exists or not in an image), and experience (algorithm training on existing images).

What is deep learning and how is it different from machine learning?

Deep learning, also known as deep neural networks are set algorithms inspired by working principals of human brain where it learns to identify patterns in data for decision making.Deep learning is a sub field of representation learning, which in fact, is a subfield of machine learning.It takes input data, throughout hidden layers it learns the representation of data to deliver a prediction or result. Layer by layer, it extracts high-level features from the previous layer data.For image processing, initial layers convolutional neural network identifies edges, shapes, and then objects.Unlike traditional machine learning algorithms, deep learning algorithms automatically extract features from raw data that makes them efficient.

How AI, ML, Deep learning, and data science is related?

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.Machine learning and deep learning are sub field of AI but that is not the case with data science.Data science is more about extracting insights from data to build robust IT and business strategies. It also takes care of data gathering, processing, analysis, and visualization.AI, ML, and DL focuses on model building for decision making. Data science includes model building as well and that is the intersection with AI where it requires to use statistical & probabilistic tools, mathematics, and model optimization to solve a problem.

What are the different types of Machine learning algorithms?

Supervised algorithms: Set of algorithms to learn from labelled data, e.g. images labelled with whether a human face exists in an image or not. Algorithms literally rely on supervisors (labelled data) to learn from data, e.g. regression, classification, object detection, segmentation, etc.Non-supervised algorithms: Set of algorithms to learn from data without labels or classes, e.g. set of images given to group similar images. These algorithms do not require supervisors for training and tries to represent same data in different forms, e.g. dimensionality reduction, clustering etc.Semi-supervised algorithms: algorithms that falls somewhere between above and uses both labelled and non-labelled data. Most of the data used for these algorithms are not labelled but fraction of them is labelled and algorithms tries to identify anomalies in data, e.g. anamoly detection.Reinforcement learning algorithms: Set of algorithms to learn best actions to take given a current scenario that maximizes overall reward. Here agent is trained to explore unseen options and scenarios by using existing knowledge, e.g. Q-learning, Deep Q networks (DQN), etc.

What is transfer learning?

Transfer learning and domain adaptation refer to the situation where what has been learned in one setting is exploited to improve generalization in another setting.The above definition was coined in the deep learning textbook by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.In layman terms, it is a technique to use the output of a model developed for a task as an input for another model to carry out the related task.Transfer learning is the technique where you can take pre-trained models (from academia, open source community and research institutions) and use them as starting point for a related machine-learning task. For real-world business applications where time-to-delivery and limited training data are a concern, transfer learning is very powerful.

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