Everything that You Should Know about the Machine Learning with Python Course

Machine Learning is a form of artificial intelligence that makes software applications provide accurate predictions without them being explicitly designed for that purpose. The algorithms of Machine Learning use previous data as input to forecast a new outcome value. One of the easiest ways to learn to do it is through Python. Python is a high-level object-oriented programming language that was created for a general-purpose. It has been designed to emphasize code readability using significant indentation. It aims to aid programmers to write clear and logical codes regardless of the project size that they might be working on. Therefore, Machine Learning with Python Training is one of the best courses that people opt for in today’s world.

An immersive Machine Learning Course with Python would include instructor-led sessions by industrial experts and professionals, hands-on learning experience with Python, supervised and unsupervised learning algorithms, and mastering ensembling techniques. These are the fundamental core topics that are a must in a Machine Learning Course. With the help of case studies, hands-on experiential learning can be provided. Furthermore, there would also be group discussions, Q&A sessions, exercises, assignments, and much more.

The prerequisites of doing Machine Learning are very basic. One needs to have some fundamental knowledge about programming so that understanding an advanced use of Python isn’t difficult. One also needs to have some familiarity with the components and use of statistics, so that one can understand Machine Learning in a course that doesn’t consume a lot of time. Furthermore, anyone interested in gaining knowledge about Machine Learning and has a willingness to master its algorithms for implementation in real-life business solutions. Knowledge about the fundamentals of quantitative analysis and Machine Learning is a must for software and data engineers.

Topics Taught in a Course about Machine Learning with Python

Using Python for Machine learning

Various libraries offered by Pythons such as Pandas, Numpy, Matplotlib, and Scikit-learn would be taught so that you can manipulate, preprocess, analyze, and visualize data. Defining variables, conditional statements, and sets in Python would be taught as well. Furthermore, the purpose of functions and operating them on files for reading and writing data in Python would be taught for a clear understanding. Many tools and techniques used in Python would be taught.

The Fundamentals of Machine Learning

The basics of Machine Learning are very important so that its core concepts are clear later on. An introduction to supervised and unsupervised learning would be provided in this topic. Real-life examples of Machine Learning and its effects on society would be provided to provide a better understanding. Various techniques, models, and algorithms like Regression, Classification, and Clustering would be taught as well.

Techniques of Optimization

Optimization techniques are used to find a minimum error in a Machine Learning Model. Having an understanding of them is a crucial part of Machine Learning. Some of the optimization techniques are namely, Batch Gradient Descent, RMSProp, Stochastic Gradient Descent, and ADAM. Besides this, the concepts of maxima and minima, cost function and learning rate would be taught as well.

Supervised Learning

In this topic, linear and logistic Regression with Stochastic Gradient Descent is taught using case studies based on real life. Hyper-parameters tuning through techniques like learning rate, class balance, epochs, and momentum are taught as well. Furthermore, K-NN Classification, Naive Bayesian classifiers, and Support Vector Machines are also included as a part of Supervised Learning. Everything would be taught along with hands-on experience through case studies.

Unsupervised Learning

Topics under unsupervised learning are clustering approaches such as K-means clustering and hierarchical clustering. They would be taught by experts with the use of real-life case studies. Plenty of assignments and exercises would be given as well to help you master your skills.

Ensemble Techniques

Under this topic, multiple Machine Learning algorithms would be taught to provide a better and more effective predictive performance. Ensemble techniques help to create Machine Learning models such as Decision Trees that are used for Regression and Classification, Entropy, Standard Deviation reduction, CHAID, Gini Index, and Information Gain. Ensemble techniques use many learning algorithms sp as to obtain a predictive performance that is better than the one that may be obtained by using any of the constituent algorithms alone.

Neural Networks

Understanding and applying neural networks is important for the classification of images and performing sentiment analysis.

Statistical Learning

Knowing the basics of statistics is very important for gaining a deep understanding of Machine learning. Therefore, mean, median, mode, distribution of data (variance, standard deviation, and inter-quartile range), exploration and measurement of data through simple graphics analyses, probability, Bayes’ theorem, and much more are included in the course as well. Hypothesis Testing is also included in this topic. Furthermore, the implementation of statistical operations in Excel is taught as well.

Recommendation System

The application of the Apriori algorithm in finding out strong associations using key metrics such as Support, Lift, and Confidence would be taught in this topic. Defining UBCF and IBCF and using them in recommender engines would also be taught. Cold-start problems are included in the Machine Learning course. Many real-life case studies would be provided for studying the building of a recommendation engine. Furthermore, recommendation techniques, collaborative filtering, content-based filtering, hybrid RS, and performance management would be taught in this course too.

In the recent past, there has been an exponential growth in Big Data and its analysis which has changed the entire operating pattern of businesses all around the world. Python is one of the best programming languages that are preferred by top companies for providing predictive analysis of Big Data. That is so because Python has very clear syntax and is easily readable and understood. The Machine Learning industry is growing at a great rate and shortly, almost every company would be using it to gain a better and more effective performance. Therefore, a career in Machine Learning with Python would prove to be very beneficial.