Nowadays Machine Learning is trending in the market. As we see everywhere ML is present, from google map to youtube recommendation. Due to this growing technology lots of people are moving towards ML. If you are also moving towards ML you should understand the syllabus of machine learning

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What is Machine Learning?

Artificial intelligence(AI) has a child that is Machine Learning(ML). ML is an efficient library, a computer can easily learn from data and then that computer makes a decision and you don’t want to program it repeatedly 

Machine Learning is a concept in which it learns itself from historical data and can make decisions.
For this we have popular algorithms like Decision Trees, Logistic Regression, SVM, Random Forest,etc.

You can try to build some basic applications using ML like Fraud detection, chatbots, self-driving cars, shopping recommendations, and medical diagnosis as beginner.

7 Stages of ML in syllabus of machine learning

When you start working on ML projects then you have to follow steps:

1. You will Collect data 
2. You Prepare and clean data (also called as Data cleansing process) 
3. You have Choose the right algorithm or model 
4. You have to Train the model 
5. After that you have to test model to check performance of model 
6. Then you Improve and fine-tune the model 
7. This model you can use for prediction and real time work 

What You Learn in syllabus of machine learning

Math

• Statistics  

Python Libraries including:

• Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn 

ML Methods

• Guess numbers (Regression) 
• Guess types (Classification) 
• Group data (Clustering) 
• Learn by doing (Reinforcement) 

Check Model

• Right or wrong (Confusion Matrix) 
• Test better (Cross-Validation) 
• Learn better (Gradient Descent) 
• Not too much, not too little (Overfitting/Underfitting) 

Deep Learning

• Brain-like models 
• For images (CNN) 
• For text/time (RNN) 
• This is for smart language 

The Real Uses are like

• Text (NLP) 
• Images (Vision) 
• Suggestions (Recommend) 

Projects

• You Can Make full ML apps 
• You can Share online (Deploy) 

Machine Learning with Python Syllabus

In a machine learning with python course or syllabus of machine learning you first learn python’s basics like datatypes, conditional statements, loops, functions, and OOPs. After that we handle data with pandas and numpy. Then we can handle data with pandas and numpy. And in an easy way we make charts using matplotlib and seaborn.

In the syllabus of machine learning, we learn models like Regression, clustering, Decision Trees, Random Forest, SVM, regression, classification and KNN. The basic concept of deep learning we can learn with TensorFlow/Keras and NLP.

At the end we first test and improve our model then you can work on real projects. Basically this course teaches you how we use python to build smart models so they can solve real world problems.

ML Fundamentals in syllabus of machine learning

It’s important to learn the basics concepts first then start learning advanced machine learning concepts. You can start with data preprocessing like fixing missing values, scaling and normalization. Also learn feature engineering.

Regression methods like linear and logistic you can learn. Also you focus on classification, decision tree, random forest, naive bayes IN Classification. You can learn K-mean clustering and DB scan if you want to group data or generate data clusters.

At last you can improve your models with regularization, hyperparameter tuning.

Advanced Topics in syllabus of machine learning

ML Projects for Practice In syllabus of machine learning

There are some easy projects like housing price prediction, spam email check, stock market prediction, and movie suggestion system.

You can also do projects like image recognition, chatbots, customer leave prediction, fraud detection, fake news check, and social media sentiment.

Also there are some good project ideas like product suggestions, dynamic pricing, virtual try-on, medical image check, disease spread prediction, energy use forecast, voice emotion detection, driver sleep detection, resume check system, self-driving car lane detection and text summarization.

Roadmap: Step-by-Step Path to Learn ML

• • Language you should start to that is python or R Language 
  • Then you should go through math concepts including statistics, regression , classification,probability,etc.
  • Also then doing some projects which are relevant 
  • Understanding concepts of Machine learning with Deep learning and NLP 
  • Work on industry based real time projects

• Push code on github and deploy code on cloud 

• Mock Interview and CV preparation
   

FAQs (Frequently Asked Questions)

Question 1.  What are the 4 types of machine learning?

Answer: There are basically 4 types of machine learning which we learn they are like:

• Supervised 
• Unsupervised 
• Reinforcement 
• Semi-supervised 

Question 2. Can I learn machine learning in 3 months?

Answer: You can definitely learn the basic things in three months at technogeeks but if you want to deep inside ML concepts then it will take time. For that, enroll now at technogeeks.

Question 3. Is machine learning full of hard math?

Answer: ML is not just full of math but has some basic concepts of math. But at technogeeks we give you knowledge about all things in detail.

Question 4. Is machine learning only about coding?

Answer: ML (Machine Learning) is not just about coding but it has a little bit of basic concepts of math also. AT our technogeeks which give knowledge of every required concept.

Question 5. What is the best language for machine learning?

Answer: For machine learning we can go with python language.

Question 6. Do I need machine learning for NLP ?

Answer: No, not always you need machine learning for NLP but if you want to make NLP better then definitely I require ML for NLP.

Career Scope in Machine Learning

In ML a good career you can make. Companies in IT, healthcare, finance, e-commerce, and cybersecurity have demand.

Roles are:

• Role like machine learning engineer is good – ₹8-20 LPA CTC for 0-5 years experience 
• Data engineer Roles also good salary roles – ₹7-18 LPA CTC for 0-5 years experience 
• AI Engineer job role in companies is also demanding – ₹10-25 LPA CTC for 0-6 years experience
• Lots companies demand for Deep Learning Specialist – ₹12-30 LPA CTC for 0-7 years experience 
• Research Scientist also good role for you 

Average salaries:

• • In India: ₹6 LPA to ₹15 LPA (depends on skills & experience) for 0-5 years experienced professionals 

• US: $100k to $150k per year for 0-7 years experienced professionals
   

Conclusion

So using this syllabus of machine learning blog we get a strong understanding of ML. As we go into details from basic to advanced in this blog, like projects, roadmap, FAQs, and career scope.

At Technogeeks IT Training Institute students get deep knowledge related to every concept from basic to advanced, do projects and get hired. If you are thinking of making a career in ML and learning concepts in the syllabus of machine learning this is the best time for you to join technogeeks.

Want to start a career and for expert guidance in projects at technogeeks call us at +91 8600998107 / +91 7028710777 For more details.