What is Road Map For Machine Learning (ML)
Hello, Guys today We are going to explore the world of Machine learning , Deep Learning & Artificial intelligence, and also which are the best algorithms of AI & ML . Which languages possess the to run the AI & ML algorithm in an efficient way. we explore what is ML and Dl?, What is Roadmap to learn Ml & DL ?. Which IDE is best for ML?. Which Project should do in ml? .Which book should be used. How much math is used ?.what is the future of ML ?.So let's start exploring.
Machine Learning
What is the Mean of Machine Learning?
According to my Opinion, Machine Learning is defined as The Operator who will teach the Machine one time by using a specific Algorithm after that according to input the machine automatically detects, which operation do on the Data or input, and The Output is shown. The Main Aspect of Machine Learning is the Algorithm that we are using, which has to efficient and more accurate. For machine definition, it is a type of AI that Predicts the output using some algorithm.
 which portion of Math Is Required For ML
The Math used in Machine Learning is Linear Algebra, Statics and Probability Theory, MultiVariate Calculus, And Optimization. To predict the output the math is important or we can say math is the pillar of Machine learning and Deep Learning. The First Approach used in Machine learning is Black Box Approach which analyzes the Input and predicts that what should be the output. The Black Box Approach is developed by the statistics and probability theory. also, The Linear Recursion is used to compare the Input and Output ( Y = Mx + c ). There are many Models like Linear, Evaluating Model, Logical Model, Geometric Model & Probability Model. Eg of the Probability model is Naive Bayes, Logical Regression.
So, First Step To learn Machine Learning is Math ( Statistics & Probability ).then Go forward about to the Algorithm used in Machine Learning, Because the algo is developed base on the Math Laws of Probability and Statics.
Introduction To ML
Below Is the Flow Chart of working on Machine Learning & how we can build and Machine Learning Model. After completing the Math Portion, we need to just master these 8 Steps to becoming Advanced ML and DL. Python is the best Language for ML Becuasues it Can be used for Data Analysis And many Libraries are present for Ml like Numpy, Pandas, Other also.
Gathering Data

Preparing the Data
Preparing the Data

Choose The Model

Train The Machine
Train The Machine

Evaluate the Output

Evaluate the Output

Hyper Parameter Training


Predictions
Gathering Data
Initially Collect All The Data. The Large Data You Collected you can Create a much Better Model and Best result of Predictions.
Preparing the Data
Now Analysis The data and Convert The Raw Data Into some specific Format that the Model easily understands.
Choose The Model
Choose the Model which supports your data and what should be your output.The model are like (
Linear, Evaluating Model, Logical Model, Geometric Model & Probability Model. Eg of the Probability model is Naive Bayes, Logical Regression.)Train The Machine
Train The Model means adding an algorithm in the Model that is necessary for the Predictions.
Evaluate the Output
The Output will help to evaluate which model will work efficiently and best prediction. It means That Output will determine which algorithm should be applied to processed the data.
Hyper Parameter Training
This is Phase where the Machine will Learn the new aspects and adapted such a new thing according to output.
Predictions
The Prediction are The Outcome,EG If we developed a model for Face recognition ,The Model has data of face i.e. name and another thing. When we give input that is when any face detected the model will recognize that face in databased and if fond predict which would be the name of that face and if not then add that face.
This is Path To learn Machine Learning And To become Advanced In It, Continuously Read...
Advanced ML
There is not any specif way to become a master in ML. To Master ML , we Need continuously Focused on The Concept of math which is used in Machine Learning, and Read the New Model Implemented by the Other ML developers. Implement All the model that You Get an idea of all model, that which model should use where. After getting all the Knowledge developing a new model of your ideas like Traffic monitoring system , Face Recognition, Facial Activity, Motion, Emotion Detector. After doing many projects You Become The Maters and Advance in Machine Learning.
The Machine Learning the Future of Technology and Robotics, where all the works are doing by the robots and the manpower will reduce and the strength of Opraot will increase. Machine learning is used to reduce the work of the operator and the machine will automatically do work eg. if the server is receiving and random data , and the operator required to always sort the new data, but when we teach the machine when the new data receive then do this operation .i.e do sorting. Their operator work is reduced so we use the machine learning with some algorithm that prediction condition.
FAQ
QUE 1): Which is the best book for machine learning?
ANS: Machine Learning With Keras and Tecnserflow. is the Best Book For m l.
QUE 2): Which should be used for mathematic of Machine Learning?
ANS: Math For Machine Learning By Mac p .
QUE 3): What is the project for ML?
ANS: Traffic monitoring system, Face Recognition, Facial Activity, Motion, Emotion Detector.Thease are some projects on Ml.
QUE 4): Which language supports Machine Learning?
ANS: Python, Lisp, Java, Prolog Are the Best Programming languages that Support Machine learning concepts . Python is best because it has the best libraries which make it easy to learn and implement.