Machine Learning Certification Course

Machine Learning has become a booming technique in the digital world. Machine Learning is considered as an application of Artificial intelligence (AI). Machine Learning mainly focuses on the study of the computer algorithms that mainly provide ways to access the data and also helps them to learn for themselves. It provides the capability for the computers to learn without being explicitly programmed. This most prominent technique will also identify patterns and make decisions with a little part of human intervention. 

Topics Covered in this module are:

• What is Machine Learning
• History of Machine Learning
• Working on Machine Learning
• Key features of Machine Learning
• Need for Machine Learning
• Classification of Machine Learning
• Advantages of using Machine Learning

Life Cycle of Machine Learning describes how Machine Learning Systems work.  Machine Learning mainly focuses on the study of the computer algorithms that mainly provide ways to access the data and also helps them to learn for themselves. This most prominent technique will also identify patterns and make decisions with a little part of human intervention. The main aim of the Machine Learning life cycle process is to find an exact solution to a project or a problem.

Topics Covered in this module are:

• Data Gathering
• Data and its processing
• Data Wrangling
• Data Analysis
• Training the Model
• Model Testing
• Deployment

In other terms, Data Preprocessing can be explained as Data and its processing methods. Data Preprocessing is a mechanism in which the raw data is filtered and makes it exactly suitable for a Machine Learning model. It is the first and foremost step that is very crucial while creating the Machine Learning Model. Data and its processing methods play a very major role in building the ML model.

Topics Covered in this module are:

• Data Collection
• Importing Libraries
• Preparing the data 
• Finding Missing Data
• Input the data
• Data processing
• Data Encoding
• Data Splitting into test and training set
• Scaling operation
• Data Output
• Storage unit

Supervised Learning is a type of learning method in which the machines are trained with the given labeled data. Based on that labeled data machines are going to predict the output. For example, it is a kind of matching the given input data with the desired output options. In the Supervised Learning method, the supervisor will teach the machines to predict the accurate output for the given input data. It is the same as the student-teacher lecture classes. In the mathematical format supervised learning is f(x) = f(y).

Topics Covered in this module are:

• How the Supervised Learning method works
• A sequence of steps involved in the Supervised Learning
• What is Regression
• What is Classification
• Different types of Regression techniques
• Regression vs Classification
• Advantages of Supervised Learning

Unsupervised Learning is different from that of the supervised learning method. Unsupervised Learning is a Machine Learning Technique in which the Machine Learning models are trained using unlabelled datasets and they are allowed to act on data without the presence of a supervisor. This ML Technique cannot be applied directly to classification or regression problems because there is no input data. The main aim of Unsupervised Learning is to group the data based on the similarities and also find the exact structure of a dataset. This mechanism works the same as human – Why is it so? Because the machine thinks on its own and gathers useful insights from the data.

Topics Covered in this module are:

• How the Unsupervised Learning technique works
• A sequence of steps involved in the Unsupervised Learning
• What is Clustering
• What is Association
• Unsupervised Learning Algorithms
• Advantages of Unsupervised Learning

Linear Regression is the most popular Machine Learning Algorithm. This statistical method is mainly used for predictive analysis. Linear Regression algorithm is used to make predictions for numerical values and continuous values. The linear regression method is also used to find the linear relationship between dependent and independent variables. Moreover, Linear Regression provides the sloped straight line to represent the relationship between the variables. 

Topics Covered in this module are:

• What is Linear Regression
• Types of Linear Regressions
• Linear Regression Line
• Gradient Descent in Linear Regression
• Linear Regression using R
• Multiple Regression using R
• Negative Linear Relationship
• R-Square Method
• Linear Regression using PyTorch
• Linear Regression Assumptions

Classification is the most essential algorithm of supervised Machine Learning. Classification is defined as the method used to classify things. It is mainly used to identify the different categories in the new observations. 

Topics Covered in this module are: 

• What is Classification Algorithm
• Different types of Classification
• Multiclass Classification
• Binary Classification
• Data Mining
• Discriminative Category 
• Generative Category
• Evaluating Classification Model

Logistic Regression is the most popular Machine Learning Algorithm. This statistical method is mainly used for predictive analysis. Linear Regression algorithm is used to make predictions for numerical values and continuous values and for predicting the categorical dependent variable. The logistic regression method is also used to find the linear relationship between dependent and independent variables. 

Topics Covered in this module are:

• What is Logistic Regression
• What is the Sigmoid Function
• Logistic Regression Assumptions
• Different types of Logistic Regressions
• What is Cost Function
• Data Preprocessing
• How to fit Logistic Regression to the training set

Naive Bayes Classifier Algorithm is the easiest Supervised Learning Algorithm that is completely based on the Bayes theorem. The Naive Bayes algorithm is mainly used for solving classification problems. It is mainly used for text classification and also helps in building Machine Learning models quickly. 

Topics Covered in this module are:

• Overview of Naive Bayes Classifier Algorithm
• Why the name Naive Bayes has chosen
• What is Bayes Theorem
• Bayes Assumptions and Naive Assumptions
• Gaussian Naive Bayes Model
• Different types of Naive Bayes Models

Decision Tree is a powerful tree structure and the most popular tool for prediction and classification. A decision tree is completely a tree structure like a flowchart in which each internal node represents a test on the attribute. In this tree, each branch represents the output of the test and each leaf node represents the class label.

Topics Covered in this module are:

• What is a Decision Tree
• Decision Tree Construction
• Decision Tree Representation
• Implementation of Decision Tree using Python
• Decision Trees Regression
• Advantages of using Decision Tree

K-Means Clustering Algorithm is a method used to perform Vector Quantization. It is a simple and easiest Unsupervised Machine Learning Algorithm. This algorithm is mainly used to identify k number of centroids and also allocates each data point to the nearest cluster. K is the number of clusters identified in the data using an algorithm so this is the reason why it is called K-Means Clustering. 

Topics Covered in this module are:

• Introduction to K-Means Algorithm
• Working of K-Means Algorithm
• What is clustering
• Python programming in K-Means Algorithm

It is also another Unsupervised Learning Algorithm it is mainly used to group the unlabeled data objects with similar characteristics. It groups unlabeled data objects into the groups called clusters. Each cluster will be different from another cluster and the objects present within the cluster will be similar to each other. This Algorithm is the very simple and most popular algorithm of Unsupervised Machine Learning.

Topics Covered in this module are:

• Overview of Hierarchical Clustering
• Divisive Hierarchical Clustering
• Agglomerative Hierarchical Clustering 
• What are Dendrograms
• Advantages of using Hierarchical Clustering

The Random Forest Algorithm is a part of the Supervised Classification Algorithm. This Random Forest Algorithm is capable of performing classification and regression tasks by using a number of decision trees. Random Forest makes use of the technique called bagging to perform classification and regression tasks. Random Forest technique is the best approach for both classification and regression. You will get to know in detail the procedure of Random Forest and also learn the implementation of Random Forest using Python.

Topics Covered in this module are:

• What is Random Forest Regression
• Bagging Classifier
• Ensemble Classifier
• How to predict the results 
• Random Forest Assumptions
• Working of Random Forest Algorithm
• Why use Random Forest

Reinforcement Learning is a field in Machine Learning it provides information about how software agents ought to take different actions in an environment to maximize the cumulative reward notation. It is one of the most prominently used Machine Learning paradigms as Supervised Learning and Unsupervised Learning. The Reinforcement Learning method is employed by various machines and software to find possible paths. 

Topics Covered in this module are:

• What is Reinforcement Learning
• Different types of Reinforcement learning
• Key elements of Reinforcement Learning
• Various approaches used to implement Reinforcement Learning
• What is the Bellman Equation
• Terms used in Reinforcement Learning
• Markov Decision Process
• Reinforcement Learning vs Supervised Learning
• Practical Applications of Reinforcement Learning

Natural Language Processing’s main aim is to make computers understand the natural language. This needs to understand the unstructured type of data and it is very difficult for the computers to perform that task. In Machine Learning, complex problems can be solved by breaking them into a number of small problems and then finds tokens or models for each problem and then integrate these tokens or models. The same process is followed in the NLP pipeline. The sequence of steps is involved in Natural Language Processing to understand the unstructured category of data. 

Topics Covered in this module are:

• What is Natural Language Processing
• History of NLP
• Components of Natural Language Processing
• Different Phases in Natural Language Processing
• What is NLP Pipeline
• Gain insights on NLP Libraries and APIs
• Computer Language vs Natural Language
• NLP advantages
• Text Processing
• Stemming process
• Lemmatization

Artificial Neural Networks refer to a set of algorithms or a system of neural networks that are designed mainly to recognize patterns and analyze data to perform specific tasks. A neural network is also defined as a circuit of neurons and they learn to perform various tasks by considering examples. Artificial Neural Networks are mainly used to capture highly parallel computations using distributed representations. 

Topics Covered in this module are:

• What is an Artificial Neural Network
• Working of Hybrid Systems
• What are activation functions
• Implementation of Artificial Neural Networks
• Convolutional Neural Networks
• What is Generative Adversarial Network
• Recurrent Neural Networks
• What is Padding
• What is mnist dataset
• What are Gated Recurrent Unit Networks

Machine Learning mainly focuses on the study of the computer algorithms that mainly provide ways to access the data and also helps them to learn for themselves. This most prominent technique will also identify patterns and make decisions with a little part of human intervention. Furthermore, It also provides the capability for the computers to learn without being explicitly programmed. There are various applications of Machine Learning in different industrial sectors. You will get through the different applications of Machine Learning in this section. 

Topics Covered in this module are:

• Traffic Predictions
• Automatic Language Translation
• Stock Market Trading
• Virtual Personal Assistant
• Self-Driving Cars
• Speech Recognition
• Image Recognition
• Product Recommendations
• Online Fraud Detection
• Malware Filtering
• Email Spam
• Medical Diagnosis