Machine Learning Institute, Computer Institute in Delhi

2. //ABOUT PROFESSIONAL TRAINING Your Mentor for Best Professional Programs Training Veridical provides a unique chance for College or University graduates / Post graduates students to get professional training in a field related to their career or professional qualifications. Experience Since 2010, imparting professional training to Young Students, Professionals, Home makers & Entrepreneurs.

3. MACHINE LEARNING WITH PYTHON

4. // Machine Learning (ML) ? • Machine learning (ML) is a type of Artificial Intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. • Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and Predictive maintenance.

5. //Importance of Machine Learning (ML) • Machine learning gives enterprises a view of trends in customer behaviour and business operational patterns, as well as supports the development of new products. • Machine learning has become a significant competitive differentiator for many companies, such as Facebook, Google Uber & ……, make machine learning a central part of their operations.

6. //Machine Learning used for (ML) ? • Customer Relationship Management. CRM software can use machine learning models to analyse email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses. • Business Intelligence. BI and Analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points and anomalies. • Human Resource Information Systems. HRIS systems can use machine learning models to filter through applications and identify the best candidates for an open position. • Self-Driving Cars. Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver. • Virtual Assistants. Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.

7. // How does ML work? • The Machine Learning process starts with inputting training data into the selected algorithm. Training data being known or unknown data to develop the final Machine Learning algorithm. The type of training data input does impact the algorithm, and that concept will be covered further momentarily.  • New input data is fed into the machine learning algorithm to test whether the algorithm works correctly. The prediction and results are then checked against each other. • If the prediction and results don’t match, the algorithm is re-trained multiple times until the data scientist gets the desired outcome. This enables the machine learning algorithm to continually learn on its own and produce the optimal answer, gradually increasing in accuracy over time.

11. UNSUPERVISED LEARNING

12. REINFORCEMENT LEARNING

14. // LEARNING OUTCOME • Learn the basics of learning problems with hypothesis and version spaces • Understand the features of machine learning to apply on real world problemsCharacterize the machine learning algorithms as supervised learning and unsupervised learning & Apply andanalyze the same. • Analyze and design the genetic algorithms foroptimization engineering problems

15. // Project Topics • FRAUD DETECTION PROJECT • BITCOIN PRICE PREDICTOR • LOAN PREDICTION • REAL ESTATE • TRANSPORT MAP MOVEMENT (UBER / OLA etc.) • Many more …………

16. Aggarwal Prestige Mall, 5th Floor-512,Rd. Number 44, Rani Bagh, Pitampura, Delhi-110034 # 93195 93915 I 93195 94915