Machine Learning Course

This program introduces the basic concepts of supervised and unsupervised machine learning. It will teach you how to create your machine learning product from scratch. Are you interested in deploying an application powered by machine learning? If so, this program can be a good fit for you! 

We provide various payment options to make the program work for you: 

• Full/Up-front discount
• Installment payment option (different maturity options)
• Income Share Agreement (ISA)
• Student Loan

Our program offers machine learning training in addition to Deep Learning and NLP in the class. This program is the best fit for an intermediate-level trainee with a background in IT. A student in this program will need motivation, commitment, discipline, and a willingness to work hard. With the right mindset, you can distinguish yourself as an IT expert in this field! 

The Machine Learning Program is meant to equip you with the advanced skills necessary to pursue a career as a machine learning engineer. 
Using performance measurements, you will analyze and update machine learning models in a production context such as a web application during Machine Learning training.

As many businesses build machine learning solutions, the demand for engineers capable of deploying machine learning models to a worldwide audience grows. 
The benefits of participating in this profession are far too significant to ignore – it will be a growth field for many years to come.

• SQL
• Linux (Shell Scripting)
• GIT
• Phyton

Billions of data bytes propel businesses to deploy machine learning to stay relevant in today’s business climate. If you have an interest in data, automation, and algorithms, machine learning can be an excellent career choice for you! Job duties include spending time evaluating massive amounts of data to apply and automate them. A machine learning career will remain in high-demand for the foreseeable future – it’s predicted to continue to be a high-growth field. 

Machine learning applies to various sectors and businesses, and its application is expected to develop over time. These are six examples of machine learning in action.

• Image recognition
• Speech recognition
• Medical diagnosis
• Statistical arbitrage
• Predictive analytics
• Extraction

The following are the key distinctions between Artificial Intelligence (AI) and Machine Learning (ML): 

• Linear Regression
• Logistic Regression
• Decision Tree
• Naive Bayes
• kNN

Only mathematics and a small number of statistics are required to grasp the fundamental concepts of machine learning. However, to utilize machine learning techniques to solve a problem or train a model, programming competence is required. 

AI is tasked with achieving the conditions necessary for a successful run. On the other side, machine learning aims for maximum accuracy to enable artificial intelligence. In other words, machine learning focuses on analyzing many parts of data to help AI make better decisions. 
AI has a broader scope than ML. AI is a goal-oriented discipline that includes a pre-installed intelligence system. However, we cannot deny that AI is meaningless without machine learning learnings. They absolutely complement one another to produce high-quality products.

Machine Learning is a branch of Artificial Intelligence that is frequently used to predict and classify data. There are two broad categories of learning: supervised and unsupervised. 

• Unsupervised learning is included to find patterns in data to facilitate processing or cluster similar samples.
• Supervised learning entails developing a model that can subsequently be used to predict or classify new data.

For instance, you can train a model by inputting the house’s length, height, and width and outputting the roof dimensions. And, if you provide enough data, the model can make predictions about the roof dimensions of a house whose roof dimensions are unknown given the house dimensions.

Additionally, you can use it to classify data. For instance, you can train a model to detect a dog’s face in a photograph by presenting it with hundreds of instances and counter-examples.

Data science and machine learning are like a room and a house. Machine learning is a subset of data science, but data science is not always machine learning. Machine learning modeling only makes up a portion of a data science career. 

The Clarusway Data Science course includes Module 1 (Data Analytics) and Module 2 (Machine Learning, Deep Learning, and NLP) programs, but the Machine Learning course is only Module 2.

NLP (natural language processing), like machine learning and deep learning, is a subfield of AI. It allows computers to understand, interpret, and manipulate human language. 

Tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection, and semantic relationship detection are some of the basic NLP tasks.

In general, NLP tasks break down language into smaller, more basic parts, try to figure out how the parts work together, and look into how the parts work together to make sense.

These fundamental tasks are usually applied in higher-level NLP capabilities such as:

• Content categorization
• Topic discovery and modeling
• Corpus Analysis
• Contextual extraction
• Sentiment analysis
• Speech-to-text and text-to-speech conversion
• Document summarization
• Machine translation

• Supervised learning (Task-Driven)
• Unsupervised learning (Data-Driven)
• Reinforcement learning (Learn from Errors)