Hi, and welcome to my course Android Game Development for Beginners. I am Sandip, and I’ll be guiding you through the process of creating your very own Android games, using Android Studio and Java. Sound exciting? Let’s get started. ******* Some Amazing Reviews From Our Learners ******* ★★★★★ i find this course simple but very clear and effective. good job! ★★★★★ Course delivers on what it advertises. Quickly and easily explains essential functionality for android game development. ★★★★★ Great course, I found the format worked really well and Sandip was really quick to respond when I had a question. I already have some basic android development knowledge but I had never made a game before, now I'm confident I can start making my own games. ★★★★★ Very useful. I have been searching for weeks to find a good OOP course. Sandip shows how to implement the app first then moved into OOP so you can understand the differences. ★★★★★ So clear for step by step, cheers! ★★★★★ Great course, i had lots of fun learning about android development. I am working on my very own app at the moment. Instructor is willing to help you if your stuck. ★★★★★ I got the opportunity to know how to build a game and truly speaking it was amusing The instructor made the tutorial easy and attractive ★★★★★ Hello, I loved the course as it was well demonstrated with practical point of view. It is must for every beginner in android game development. Loved it. Will be waiting for more!! ******* Course Overview ******* This course will introduce some key elements of game programming. I will be covering basic XML Layout designing, Frame by Frame Animation, Object Oriented concept for Animation, detecting Touch Event and Collision, showing Score and Health Indicator. Score is displayed in GameOver screen once the game is over. From the GameOver screen, you can either Restart the game or Exit from it. I will show you how you can save your personal best score using Android SharedPreferences class. I believe the best way to learn game programming is by making one. So, I will walk you through the process of creating a complete Android game, from start to finish, step by step in a completely hands-on fashion, sharing some super useful resources along the way. As we move on, we will be creating a small but complete game called “Plane Shooter”. I'll show you step-by-step how to Create Different Levels for your game and add features like changing game background and increasing the speed of the Planes for every succeeding level to make your game more interesting and fun, in the easiest way possible. This course improves your problem-solving ability as I invite you to find the solution yourself so that you can think and solve your coding problems like a pro. I am also available to support you along the way as much as I can. I'll do the heavy lifting for you so that you can have an enjoyable learning experience and focus on implementing the game features that you want. This course doesn't stop here. I'll demonstrate step-by-step how to monetize your app through Google AdMob Banner and Interstitial ads so that you can start getting some revenue. You can download all the project source codes once you purchase this course which you're free to use for your personal work. As a motivation for you, I've included few real apps made by my students with some help from this course. https://www.udemy.com/course/android-game-development-for-beginners-learn/?couponCode=FREE_JAN_21

Uplatz offers this in-depth course on Machine Learning concepts and implementing machine learning with Python. Objective: Learning basic concepts of various machine learning methods is primary objective of this course. This course specifically make student able to learn mathematical concepts, and algorithms used in machine learning techniques for solving real world problems and developing new applications based on machine learning. Course Outcomes: After completion of this course, student will be able to: 1. Apply machine learning techniques on real world problem or to develop AI based application 2. Analyze and Implement Regression techniques 3. Solve and Implement solution of Classification problem 4. Understand and implement Unsupervised learning algorithms Topics Python for Machine Learning Introduction of Python for ML, Python modules for ML, Dataset, Apply Algorithms on datasets, Result Analysis from dataset, Future Scope of ML. Introduction to Machine Learning What is Machine Learning, Basic Terminologies of Machine Learning, Applications of ML, different Machine learning techniques, Difference between Data Mining and Predictive Analysis, Tools and Techniques of Machine Learning. Types of Machine Learning Supervised Learning, Unsupervised Learning, Reinforcement Learning. Machine Learning Lifecycle. Supervised Learning : Classification and Regression Classification: K-Nearest Neighbor, Decision Trees, Regression: Model Representation, Linear Regression. Unsupervised and Reinforcement Learning Clustering: K-Means Clustering, Hierarchical clustering, Density-Based Clustering. Detailed Syllabus of Machine Learning Course 1. Linear Algebra Basics of Linear Algebra Applying Linear Algebra to solve problems 2. Python Programming Introduction to Python Python data types Python operators Advanced data types Writing simple Python program Python conditional statements Python looping statements Break and Continue keywords in Python Functions in Python Function arguments and Function required arguments Default arguments Variable arguments Build-in functions Scope of variables Python Math module Python Matplotlib module Building basic GUI application NumPy basics File system File system with statement File system with read and write Random module basics Pandas basics Matplotlib basics Building Age Calculator app 3. Machine Learning Basics Get introduced to Machine Learning basics Machine Learning basics in detail 4. Types of Machine Learning Get introduced to Machine Learning types Types of Machine Learning in detail 5. Multiple Regression 6. KNN Algorithm KNN intro KNN algorithm Introduction to Confusion Matrix Splitting dataset using TRAINTESTSPLIT 7. Decision Trees Introduction to Decision Tree Decision Tree algorithms 8. Unsupervised Learning Introduction to Unsupervised Learning Unsupervised Learning algorithms Applying Unsupervised Learning 9. AHC Algorithm 10. K-means Clustering Introduction to K-means clustering K-means clustering algorithms in detail 11. DBSCAN Introduction to DBSCAN algorithm Understand DBSCAN algorithm in detail DBSCAN program https://www.udemy.com/course/machine-learning-concepts-and-application-of-ml-using-python/?couponCode=ML_FULL_UPLATZ