Machine Learning Certification Training

[vc_row full_width=”stretch_row” css=”.vc_custom_1559286923229{background-color: #f6f6f7 !important;}”][vc_column width=”1/2″][vc_tta_accordion color=”peacoc” active_section=”1″][vc_tta_section title=”Introduction to Data Science” tab_id=”1559286383409-ab730398-6c03″][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Data Extraction, Wrangling, & Visualization” tab_id=”1559286522681-3bf94e12-e7b7″][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Introduction to Machine Learning with Python” tab_id=”1561382593569-b1979b66-b066″][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Supervised Learning – I” tab_id=”1561382595833-dd54d407-26c0″][vc_column_text]

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?

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Dimensionality Reduction” tab_id=”1561382597303-5168678c-55b9″][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Are the Different Types of Machine Learning?” tab_id=”1584361885436-862ac8d3-2230″][vc_column_text]In supervised machine learning, a model makes predictions or decisions based on past or labeled data. Labeled data refers to sets of data that are given tags or labels, and thus made more meaningful.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Is Overfitting, and How Can You Avoid It? ” tab_id=”1584361887052-1672639f-ca2e”][vc_column_text]Overfitting is a situation that occurs when a model learns the training set too well, taking up random fluctuations in the training data as concepts. These impact the model’s ability to generalize and don’t apply to new data.

When a model is given the training data, it shows 100 percent accuracy—technically a slight loss. But, when we use the test data, there may be an error and low efficiency. This condition is known as overfitting.

There are multiple ways of avoiding overfitting, such as:

  • Regularization. It involves a cost term for the features involved with the objective function
  • Making a simple model. With lesser variables and parameters, the variance can be reduced
  • Cross-validation methods like k-folds can also be used
  • If some model parameters are likely to cause overfitting, techniques for regularization like LASSO can be used that penalize these parameters

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Is ‘training Set’ and ‘test Set’ in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?” tab_id=”1584361888220-98f08245-bc4b”][vc_column_text]There is a three-step process followed to create a model:

  1. Train the model
  2. Test the model
  3. Deploy the model
Training Set Test Set
  • The training set is examples given to the model to analyze and learn
  • 70% of the total data is typically taken as the training dataset
  • This is labeled data used to train the model
  • The test set is used to test the accuracy of the hypothesis generated by the model
  • Remaining 30% is taken as testing dataset
  • We test without labeled data and then verify results with labels

Consider a case where you have labeled data for 1,000 records. One way to train the model is to expose all 1,000 records during the training process. Then you take a small set of the same data to test the model, which would give good results in this case.

But, this is not an accurate way of testing. So, we set aside a portion of that data called the ‘test set’ before starting the training process. The remaining data is called the ‘training set’ that we use for training the model. The training set passes through the model multiple times until the accuracy is high, and errors are minimized.[/vc_column_text][/vc_tta_section][vc_tta_section title=” How Do You Handle Missing or Corrupted Data in a Dataset?” tab_id=”1584361889390-632a6c62-76af”][vc_column_text]One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or replace them entirely with some other value.

There are two useful methods in Pandas:

  • IsNull() and dropna() will help to find the columns/rows with missing data and drop them
  • Fillna() will replace the wrong values with a placeholder value

[/vc_column_text][/vc_tta_section][vc_tta_section title=”How Can You Choose a Classifier Based on a Training Set Data Size?” tab_id=”1584361890576-0f5bfb5a-8a21″][vc_column_text]When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overfit.

For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Explain the Confusion Matrix with Respect to Machine Learning Algorithms.” tab_id=”1584361891593-5e0239ff-e4d6″][vc_column_text]A confusion matrix (or error matrix) is a specific table that is used to measure the performance of an algorithm. It is mostly used in supervised learning; in unsupervised learning, it’s called the matching matrix.

The confusion matrix has two parameters:

  • Actual
  • Predicted

It also has identical sets of features in both of these dimensions.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Is a False Positive and False Negative and How Are They Significant?” tab_id=”1584361892977-fe1edd3d-8e6a”][vc_column_text]False positives are those cases which wrongly get classified as True but are False.

False negatives are those cases which wrongly get classified as False but are True.

In the term ‘False Positive,’ the word ‘Positive’ refers to the ‘Yes’ row of the predicted value in the confusion matrix. The complete term indicates that the system has predicted it as a positive, but the actual value is negative.

So, looking at the confusion matrix, we get:

False-positive = 3

True positive = 12

Similarly, in the term ‘False Negative,’ the word ‘Negative’ refers to the ‘No’ row of the predicted value in the confusion matrix. And the complete term indicates that the system has predicted it as negative, but the actual value is positive.

So, looking at the confusion matrix, we get:

False Negative = 1

True Negative = 9[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Are the Three Stages of Building a Model in Machine Learning?” tab_id=”1584361894028-3c14e8aa-9a74″][vc_column_text]The three stages of building a machine learning model are:

  • Model Building

    Choose a suitable algorithm for the model and train it according to the requirement

  • Model Testing

    Check the accuracy of the model through the test data

  • Applying the Model

    Make the required changes after testing and use the final model for real-time projects

Here, it’s important to remember that once in a while, the model needs to be checked to make sure it’s working correctly. It should be modified to make sure that it is up-to-date.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Is Deep Learning?” tab_id=”1584361895226-aaa85597-d72b”][vc_column_text]Deep learning is a subset of machine learning that involves systems that think and learn like humans using artificial neural networks. The term ‘deep’ comes from the fact that you can have several layers of neural networks.

One of the primary differences between machine learning and deep learning is that feature engineering is done manually in machine learning. In the case of deep learning, the model consisting of neural networks will automatically determine which features to use (and which not to use).[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Are the Differences Between Machine Learning and Deep Learning?” tab_id=”1584361913929-404b765b-93dc”][vc_column_text]

Machine Learning  Deep Learning
  • Enables machines to take decisions on their own, based on past data
  • It needs only a small amount of data for training
  • Works well on the low-end system, so you don’t need large machines
  • Most features need to be identified in advance and manually coded
  • The problem is divided into two parts and solved individually and then combined
  • Enables machines to take decisions with the help of artificial neural networks
  • It needs a large amount of training data
  • Needs high-end machines because it requires a lot of computing power
  • The machine learns the features from the data it is provided
  • The problem is solved in an end-to-end manner

[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][vc_column width=”1/2″][vc_tta_accordion color=”peacoc” active_section=”1″][vc_tta_section title=”Supervised Learning – II” tab_id=”1561382561432-7f73ef2a-cc67″][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Unsupervised Learning” tab_id=”1561382561455-654071d3-eb53″][vc_column_text]

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?

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Association Rules Mining and Recommendation Systems” tab_id=”1561382611424-56181e07-6453″][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Reinforcement Learning” tab_id=”1561382613753-7c9c9136-4ca1″][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Time Series Analysis” tab_id=”1561382614729-6b63842b-62b1″][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Model Selection and Boosting” tab_id=”1561382615672-42dd66a8-6425″][vc_column_text]

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

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Are the Applications of Supervised Machine Learning in Modern Businesses?” tab_id=”1584361897749-7663733b-1db8″][vc_column_text]Applications of supervised machine learning include:

  • Email Spam Detection

    Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.

  • Healthcare Diagnosis

    By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.

  • Sentiment Analysis

    This refers to the process of using algorithms to mine documents and determine whether they’re positive, neutral, or negative in sentiment.

  • Fraud Detection

    Training the model to identify suspicious patterns, we can detect instances of possible fraud.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Is Semi-supervised Machine Learning?” tab_id=”1584361898617-2958b4b7-ed48″][vc_column_text]Supervised learning uses data that is completely labeled, whereas unsupervised learning uses no training data.

In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data.[/vc_column_text][/vc_tta_section][vc_tta_section title=” What Are Unsupervised Machine Learning Techniques? ” tab_id=”1584361899445-68f75a32-d819″][vc_column_text]There are two techniques used in unsupervised learning: clustering and association.

Clustering

Clustering problems involve data to be divided into subsets. These subsets, also called clusters, contain data that are similar to each other. Different clusters reveal different details about the objects, unlike classification or regression.

Association

In an association problem, we identify patterns of associations between different variables or items.

For example, an ecommerce website can suggest other items for you to buy, based on the prior purchases that you have made, spending habits, items in your wishlist, other customers’ purchase habits, and so on.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Is the Difference Between Supervised and Unsupervised Machine Learning?” tab_id=”1584361900499-d9e7c406-296b”][vc_column_text]

  • Supervised learning – This model learns from the labeled data and makes a future prediction as output
  • Unsupervised learning – This model uses unlabeled input data and allows the algorithm to act on that information without guidance.

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Is the Difference Between Inductive Machine Learning and Deductive Machine Learning? ” tab_id=”1584361901744-807c3dec-ef91″][vc_column_text]

Inductive Learning Deductive Learning
  • It observes instances based on defined principles to draw a conclusion
  • Example: Explaining to a child to keep away from the fire by showing a video where fire causes damage
  • It concludes experiences
  • Example: Allow the child to play with fire. If he or she gets burned, they will learn that it is dangerous and will refrain from making the same mistake again

[/vc_column_text][/vc_tta_section][vc_tta_section title=”Compare K-means and KNN Algorithms.” tab_id=”1584361902798-6d3066e6-c96d”][vc_column_text]

K-means KNN
  • K-Means is unsupervised
  • K-Means is a clustering algorithm
  • The points in each cluster are similar to each other, and each cluster is different from its neighboring clusters
  • KNN is supervised in nature
  • KNN is a classification algorithm
  • It classifies an unlabeled observation based on its K (can be any number) surrounding neighbors

[/vc_column_text][/vc_tta_section][vc_tta_section title=”What Is ‘naive’ in the Naive Bayes Classifier?” tab_id=”1584361903875-4734083e-d393″][vc_column_text]The classifier is called ‘naive’ because it makes assumptions that may or may not turn out to be correct.

The algorithm assumes that the presence of one feature of a class is not related to the presence of any other feature (absolute independence of features), given the class variable.

For instance, a fruit may be considered to be a cherry if it is red in color and round in shape, regardless of other features. This assumption may or may not be right (as an apple also matches the description)[/vc_column_text][/vc_tta_section][vc_tta_section title=”Explain How a System Can Play a Game of Chess Using Reinforcement Learning.” tab_id=”1584361904987-1d06bb02-1f9f”][vc_column_text]Reinforcement learning has an environment and an agent. The agent performs some actions to achieve a specific goal. Every time the agent performs a task that is taking it towards the goal, it is rewarded. And, every time it takes a step which goes against that goal or in reverse direction, it is penalized.

Earlier, chess programs had to determine the best moves after much research on numerous factors. Building a machine designed to play such games would require many rules to be specified.

With reinforced learning, we don’t have to deal with this problem as the learning agent learns by playing the game. It will make a move (decision), check if it’s the right move (feedback), and keep the outcomes in memory for the next step it takes (learning). There is a reward for every correct decision the system takes and punishment for the wrong one.[/vc_column_text][/vc_tta_section][vc_tta_section title=”How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?” tab_id=”1584361906181-243ae990-3031″][vc_column_text]While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:

  • If accuracy is a concern, test different algorithms and cross-validate them
  • If the training dataset is small, use models that have low variance and high bias
  • If the training dataset is large, use models that have high variance and little bias

[/vc_column_text][/vc_tta_section][vc_tta_section title=”How Is Amazon Able to Recommend Other Things to Buy? How Does the Recommendation Engine Work?” tab_id=”1584362538835-f0c10636-2d3b”][vc_column_text]Once a user buys something from Amazon, Amazon stores that purchase data for future reference and finds products that are most likely also to be bought, it is possible because of the Association algorithm, which can identify patterns in a given dataset.[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][/vc_row][vc_row css=”.vc_custom_1561629087314{background-image: url(https://www.coursesit.us/wp-content/uploads/2019/06/1_maqmu2RX4TqzAL55mgQUPA.jpeg?id=7155) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}”][vc_column][vc_empty_space height=”532px”][/vc_column][/vc_row]

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