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In-depth approach to ML easing you into the basics of ML and making you a pro out of it in no time. Grab this course now
This course teaches you about popular techniques used in machine learning, data science, and statistics. We cover the theory from the ground up basics of python to the advanced topics essential for a learning enthusiast to kick-start their journey. The course makes sure each topic must deliver a valuable amount of knowledge.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. In this course, we will cover the fundamentals of Machine Learning, such as complex algorithms, calculations, and coding libraries, in a simple, straightforward manner. Each segment of Machine Learning would be broken down and would be easy to grasp. Throughout this course, you would understand the basics of Machine Learning, and by the end of the course, you would have gained enough knowledge to be able to call yourself an expert in Machine Learning. Step by step, you will build up new skills as well as further improve your understanding of machine learning.
We will also discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, Visualization, test and train data split, and so on.
Those with prior programming experience would find that this course might clarify some concepts and aid in fully mastering Machine Learning. This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:
Section 1 - Python basics and advanced concepts(Python decorators and generators, Numpy, Pandas)
Section 2 - Machine Learning concepts - Unsupervised learning, supervised learning, and techniques like - Standard deviation, Percentiles, moment, and Quantiles, (Mean Mode median)
Section 3 – Data preprocessing - Test and train data split, data import, handling missing data, Under and over sampling
Section 4- Regression: Simple Linear Regression, Multiple Linear Regression, SVR, Decision Tree Regression, Random Forest Regression, Polynomial Regression
Section - Classification: Logistic Regression, K-NN, SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
Section 6 - Clustering: K-Means clustering, Optimal clusters
Section 7 - Reinforcement Learning: Upper Confidence Bound
Section 8 - Natural Language Processing(NLP): Introduction to NLP, Text classification using ML, Building Text classifier
Section 9 - Deep Learning: NLP, DL, DNLP, and Neural network, behind the scene calculations, Back propagation, Data representations using numbers, Activation Functions, and types
Section10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Section 11 - Web Application using Flask(basics) and Model Deployment on Flask WebApplication
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your models.
Specifically, you will learn:
How to select features based on changes in model performance
How to find predictive features based on the importance attributed by models
How to code procedures elegantly and in a professional manner
How to leverage the power of existing Python libraries for feature selection
Data Science and Machine Learning are the hottest skills in demand but are challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Data Processing, Machine Learning A-Z, Deep learning, and more?
Well, you have come to the right place.
Today Data Science and Machine Learning are used in almost all industries, including automobile, banking, healthcare, media, telecom, and others.
What is essential for a Data Science and Machine Learning practitioner is that you will have to research and look beyond normal problems, you may need to do extensive data processing, experiment with the data using advanced tools, and build amazing business solutions. However, where and how are you going to learn these skills required for Data Science and Machine Learning?
Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an in-depth understanding of the following skills,
Understanding of the overall landscape of Machine Learning
Different types of Data Analytics, Deployment characteristics of Machine Learning concepts
Python Programming skills which is the most popular language for Data Science and Machine Learning
Mathematics for Machine Learning including Linear Algebra, Calculus and how it is applied in Machine Learning Algorithms as well as Data Science
Statistics and Statistical Analysis for Data Science
Data Visualization in a greater detail
Data processing and manipulation before applying Machine Learning
Feature Selection and Dimensionality Reduction for Machine Learning models
Machine Learning Model Selection using Cross-Validation and Hyperparameter Tuning
Cluster Analysis for unsupervised Machine Learning
Using computation as the common language, we have come a long way, but the journey ahead is still long. In many real-world applications, it is unclear whether problem formulation falls neatly into fully learning. The problem may well have a large component, which can be best modeled using an ML algorithm without the learning component, but there may be additional constraints or missing knowledge that take the problem outside its regime, and learning may help to fill the gap. Similarly, programmed knowledge and reasoning may help learners to fill their gaps.
Learning enthusiasts will find this course appealing and would furnish their skill sets as well as provide weightage to their resumes.
Machine Learning – An Introduction
Machine learning is a field of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way people learn, intending to steadily improve accuracy. Machine Learning is quite an exciting field. Machine Learning allows software applications to make predictions or decisions based on models and algorithms; as the software comes across more data, it can adapt accordingly without being programmed to do so. Initially, Machine Learning was time-consuming, tedious, and inefficient, that it was regarded as unfeasible for any practical use. However, major breakthroughs in the 90s paved the way for Machine Learning to perform efficiently and eventually made machine learning feasible and be able to be used in many software services and applications. Nowadays, Machine Learning is used in various industries and organizations, including government, retail, transportation, and health care.
Machine learning is a computer’s ability to execute tasks without being explicitly programmed yet thinking and acting like machines. Their capacity to do some complicated tasks — such as collecting data from an image or video — is still far behind that of humans. Because they've been particularly patterned after the human brain, deep learning models bring an exceptionally complex approach to machine learning and are prepared to solve these challenges. Data is transferred between nodes (like neurons) in highly linked ways using complex, multi-layered "deep neural networks.”
Deep learning is a part of machine learning methods that are based on artificial neural networks (ANN) and representation learning. It can be either supervised, semi-supervised or unsupervised. Deep learning connects advancements in computer power with specialized neural networks to learn complex patterns from massive amounts of data. Deep learning's influence on the industry started back in the early 2000s when CNN's processed an approximated 10% to 20% of all checks made in the entire United States. Deep learning applications for large-scale voice recognition emerged around 2010.
Why is Machine Learning So Important?
The resurging interest in machine learning can be attributed to the fact the vast volumes and varieties of data are now available more than ever, combined with cheaper computational processing and more affordable data storage. All businesses rely on data to function. Data-driven choices are increasingly determining whether a company keeps up with the competition or falls further behind. Many sectors are now working to develop more robust machine learning models capable of evaluating larger and more complex data while delivering faster, more accurate answers on massive sizes. Machine learning algorithms help businesses identify valuable opportunities and potential risks more quickly. It has the power to unveil the value of corporate and consumer data, which enables companies to make decisions that keep them ahead of the competition. Machine learning is the ideal approach to develop models, strategize, and plan in industries that rely on large amounts of data and need a system to evaluate it rapidly and effectively. Machine Learning (ML) is applied in almost every type of industry, including retail, healthcare, life sciences, travel and hospitality, feedstock, and manufacturing. As there are many applications for Machine Learning, you would find multiple career opportunities in Machine Learning without fear of it becoming saturated.
If you’re optimistic about reaping the benefits of having Machine Learning skills under your belt, then this course is for you!
Learn a Powerful Skill from the Comfort of Your Home
This course is one of the best courses available for Machine Learning. Theoretical knowledge, although important, is not sufficient to succeed in Machine Learning. Often companies would require that you have some experience in Machine Learning first. Practical application is required if you are to master Machine Learning skills. This course will empower you by providing a way to practice Machine Learning concepts from the comfort of your home, granting you some worthwhile experience with Machine Learning. Mastering Machine Learning would make you a valuable asset for many companies to have as it is in high demand. People who are well-versed in Machine Learning can choose to become either Data Scientists, Machine Learning Engineers, or Computer Vision Specialists.
Why You Should Choose this Course?
Machine Learning is a very complex and challenging field of study. It can be very daunting to some due to this fact. However, my course does well to break down Machine Learning in a way that is easier to grasp. Backed by my experience as an application developer combined with seven years of experience of teaching IT to over 140,000 satisfied students, I believe that my in-depth knowledge of the industry and vast teaching experience would be an immense help to you in grasping the fundamentals of Machine Learning as well as mastering it.
Assisting you in mastering Machine Learning free of issues is my top priority. You would find that I adopt a unique teaching style that is distinct from others in that it is highly straightforward and simple to understand, following a step-by-step approach. If for any reason, you find the course content confusing or difficult to understand, you can contact me to clear your doubts. I will gladly be available to tend to your doubts.
What You’ll Learn:
Effective and efficient machine learning methods which are executed devoid of any issues
Issues that can be solved through Machine Learning
How Machine Learning can be used to process functions
Use Python for Machine Learning
Percentiles, moment and quantiles
Learn to utilize Matplotlib for Python plotting
Learn to utilize Seaborn for measurable plots
Learn Advance mathematics for Machine Learning
Understand matrix multiplication, Matrix operations, and scalar operations
Use Pair plot and limitations
Implement Identity matrix, matrix inverse properties, transpose of a matrix, and Vector multiplication
Implement Linear Regression, Multiple Linear Regression, Polynomial Regression, Decision Tree Regression, Random Forest Regression
AdaBoost and XGBoost regressor, SVM (regression) Background, SVR under Python
ML Concept-k-Fold validation, GridSearch
Classification-k-nearest neighbours algorithm(KNN)
Gaussian Naive Bayes under python & visualization of models
Learn evaluation techniques using curves (ROC, AUC, PR, CAP)
Implement machine learning algorithms
Model Deployment on Flask WebApplication
Natural Language Processing(NLP)
More topics coming soon
No Question Asked – Money Back Guarantee!
The main barrier towards people paying for a course to learn a daunting, challenging skill is whether it is suitable for them or whether they would be able to benefit from it. However, you can be at peace with the fact that you can opt out of this Machine Learning tutorial whenever you want to within 30 days. Basically, there is minimal risk involved with purchasing this course as it comes with a 30-day money-back guarantee. Once you purchase the course and later find that for any reason you are not satisfied with the course, you are entitled to a full refund, no questions asked.
Now that you know that you’ve got nothing to lose, what are you waiting for? Purchase this course now and get access to a Machine Learning master class that gives you a step-by-step approach to Machine Learning.
By the end of this course, you would have Machine Learning at the tip of your fingers, along with the skills necessary to enter the high-paying and in-demand field of Data Science.
Join me on this adventure today! See you on the course.