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.
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.
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)
- Deep Learning
- More topics coming soon
Who this course is for:
- Anyone curious about Machine Learning or AI
- Students who have a minimum of high school knowledge in math and who need to begin learning Machine Learning
- People with a vested interest in studying Machine Learning but have trouble understanding how to code
- College Students looking to build up a career in Data Science
- Data Analysts looking to upskill themselves through Machine learning
- Software developers or programmers looking to transition into the Machine Learning career path