Hello topic, you could easily get discouraged. There’s too

Hello Mishra. AI is a wonderful field to get into. Not only is it in high demand in the job market, it also helps you perceive the world in a whole new way. It’s great that you have a deep interest in AI. In my opinion, you’ll progress faster if you are having fun. Learning is always accelerated when you are curious and deeply interested in a particular domain or technology. First, begin by building your foundations, and then later you can specialize in specific in areas of interest. As a beginner its not advisable for you to just dive deep into every machine learning topic, you could easily get discouraged. There’s too much ground to cover and the field is progressing rapidly. Start by learning the foundational concepts. Focus on one thing for now and learn the concepts really well. Below are the basic topics that I would suggest you pick up as you begin your journey towards proficiency in AI. Learn and refresh in math ( linear algebra, numerical optimization and differential calculus), probability and statistics: I cannot stress enough how important basic statistical concepts are in understanding AI. Statistics will help you grasp many of the algorithms used such as linear regression and classification. Matrix Algebra on the other hand is important when you start manipulating large amounts of data. Additionally you need atleast a basic grasp of calculus, important concepts such as gradient descent cannot be understood without atleast a basic comprehension of calculus. Regarding materials to learn these courses I recommend that you download Stanford University’s Elements of Statistical Learning. https://web.stanford.edu/~hastie/Papers/ESLII.pdf and  Khan academy’s differential calculus course. https://www.khanacademy.org/math/calculus-home/differential-calculusProgramming: Programming is key in order to successfully implement learning algorithms. Its great that you know Java and C++, as long as you get the concepts (variables, loops, objects, methods and data structures) picking up other languages becomes easier. I advise that you pick up Python since it enables fast and natural expression of mathematical equations. It also has a fantastic ecosystem, there is virtually a Python package for almost any conceivable math function. However before you pick up Python. There are a number of ML libraries for Java which can leverage on your experience with the language, such as WEKA, MOA, Deeplearning4j, Stanford CORE NLP, Apache Singa, Mallet and Elki. These are super useful Java libraries which can be applied directly to many problems in the field of AI. I advice you to sign up for Code Academy which is a platform where you can learn to code interactively. https://www.codecademy.com/You will then need to familiarise yourself with basic Learning Algorithms: There are numerous algorithms in Machine Learning which are important to one problem or the other. There is no single model that works best for all problems. Familiarizing yourself with Linear Regression, Support Vector Machines, Dimensionality reduction, Gaussian Processes, Naive Bayes, Decision Tree’s and K-Means algorithms will definitely prove useful in the long run. I recommend you pick up the freely available book Deep Learning by Yoshua Bengio and Ian Goodfellow for this http://www.deeplearningbook.org/To have a competitive edge be sure to invest in ‘theory’: A great grasp of the theoretical topics that surround AI (i.e. theory of computation , signal processing and the artificial neuron) lays a strong foundation for a lot of the abstract areas of AI. You also need to be well versed in AI theory in order to keep up with new developments in the field. Practice: Consider getting started on some simple AI projects i.e. a chat bot, handwritting recognition or a tic-tac-toe bot. I recommend that you post your code on Github so that others see your progress and contribute new ideas on how you can advance forward. Kindly follow Siraj Rawal’s github account for interesting AI project ideas  https://github.com/llSourcell?tab=repositories

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