LIMITED TIME OFFER
Get a discounted price of $399 instead of $2999
00Hours00Minutes00Seconds
Mastering the A-Z of Data Science & Machine Learning
Get 87% OFF in our Data Science & Machine Learning Full course! Grab The Course Now at Only $399 ($2,999)
- The introduction and Applications of Data Scraping
- Essential Concepts and Algorithms in Machine Learning
- Python for Data Science and Data Analysis
-
Data Understanding and Data Visualization with Python
-
Probability and Statistics in Python
-
Feature Engineering and Dimensionality Reduction with Python
-
Artificial Neural Networks with Python
-
Convolutional Neural Networks with Python
-
Recurrent Neural Networks with Python
-
Reinforcement Learning
-
Detailed Explanation and Live Coding with Python
-
Building your own AI applications.
- People who want to enter the Machine Learning field
- People who want to become perfect in their data speak.
- People who want to learn Data Science & Machine Learning along with its implementation in realistic projects
-
People who want to learn Data Science & Machine Learning with real datasets in Data Science
- People from a non-engineering background who want to enter the Data Science field
-
Individuals who are passionate about numbers and programming
-
Business Analysts
-
Beginners in Data Science field
We work meticulously to create online tutorials with instructors who are willing to share their expertise and help you in understanding all the concepts. The aim is to create a strong basic understanding for you before you move onward to the advanced version. Detailed course notes, high-quality video content, learning assessment questions, meaningful course material, and subject-related handouts are some of the perks of this course. You are also assured of the support of a dedicated instructor every step of the way. You can approach our team in case of any queries.
A Live session is organized every two weeks to allow all students to ask their questions directly to our instructors
What The Course Entails
Testimonials
The course 'Mastering Data Science & Machine Learning" is actually 10 courses put together.
1. Python for Data Science and Data Analysis
- You start with problem-solving and finish with fancy indexing and plots in Matplotlib.
- No prior knowledge in any computer science language is assumed.
- Great fun with Python language.
-
Reasonable treatment of data science packages (NumPy, Pandas, Matplotlib, Seaborn, and Sklearn).
-
After this course, you will be a competent Python programmer as well as a reasonable expert of data science packages (NumPy, Pandas, Matplotlib).
-
This section is designed to teach you programming in general also. Therefore, shifting from this language to any other language after this section is not difficult.
2. Data Understanding and Data Visualization with Python
- This section deals with the in-depth treatment of data science packages both for data manipulation as well as data visualization.
- While Section 1 focuses more on Python language, this section focuses completely on data science packages and their efficient use.
- The packages covered in this section include NumPy, Pandas, Matplotlib, Seaborn, Bokeh, Plotly, and Folium.
-
As far as we know, this is the most comprehensive section on data understanding and visualization among the available ones.
-
Further, this section is designed to reduce the dependency on core Python language to be treated independently, as well.
-
2D and 3D visualizations, interactive visualizations, and geographic maps are also covered.
-
Proceeding in data science with being able to effectively play with the data using famous packages makes progress much worse, and this section addresses this concern.
3. Mastering Probability and Statistics in Python
- Obviously, concepts in data science are not new. In fact, it is also believed that data science is merely a renamed version of Probability and Statistics. Well, without being biased to that extent, we will say that the practical nature of applications was uncovered earlier even though the theory traces back to Probability and Statistics.
- One way or the other, knowing Probability and Statistics makes a significant theoretical as well as practical difference.
- Most of the courses on Probability and Statistics, however, fail to link the data science practices and theory by merely focusing on the axiomatic treatment of the subject.
-
We build this section by keeping the practical needs of data science in mind as well as the importance of theory.
- Wherever important, we deliberately explain and show the relationships by derivations and even through Python Code.
-
This section builds a very sound basis for understanding the classical concepts in data science as well as its more recent generalizations.
-
We start with the very basics of Probability, go through inference and estimations, link famous machine learning techniques with conditional probability, and finally, show that Deep Neural Networks indeed learn a probability function eventually.
4. Machine Learning Crash Course
- Although several concepts, or even all, fall under the umbrella of Probability and Statistics, it turns out that most of the concepts have made their own practical place, mostly derived through engineering, with the name of Machine Learning. For example, the term “overfitting” is now referring to the area of machine learning.
- Machine Learning brings its own set of practices to reach the demands of automation. Hence, mastering these concepts becomes inevitable.
- This section is actually a quick walkthrough of the concepts in Machine Learning and focuses on all the theoretical as well as practical concepts.
-
We mostly cover applications using the Sklearn Python package and build machine learning pipelines in this section.
- We also elaborate on more advanced areas of machine learning, which we later present as separate sections.
5. Feature Engineering and Dimensionality Reduction with Python
- Knowing the sections you have covered thus far certainly brings you a huge clarity of the field. But there is still one thing that brings the improvements in the results with a reasonable margin, and that is data preprocessing or data preparation.
- Most of the data science today relies on preparing the data suitable for machine learning models. An effective way of data preparation, most of the time, becomes a game-changer.
- This section focuses on data preparation for machine learning models.
- We build this section to provide an understanding of why selecting features and transforming features are important.
- We also discuss practical issues with real data, like missing values and non-numeric data.
-
We discuss the performance improvements both in terms of execution time as well as the accuracy of the models.
-
We explain the required mathematical background in a simple way.
-
Finally, all the concepts are made more easily understandable by coding relevant examples in Python.
6. Artificial Neural Networks with Python
- With the availability of a huge quantity of data as well as computation power, a relatively old machine learning model, Artificial Neural Network turns out to be the game-changer in data science.
- Artificial Neural Network can approximate almost any pattern in the data. Further, it has a much greater data utilization capacity as compared to the more classical methods.
- With the recent rise of ANNs, a lot of practical techniques are also discovered, particularly for ANNs.
- Also, working with a large amount of data brings its own challenges for learning algorithms.
- In this section, we address all these concerns and cover ANNs in depth.
-
We also introduce another framework, “TensorFlow,” for working in ANNs.
-
With this section in hand, you can now target much larger machine learning problems.
7. Convolutional Neural Networks with Python
- ANNs, in its most basic form, is not that suitable for image data and for the problems in computer vision.
- Convolutional Neural Networks (CNNs) are considered a game-changer in the field of computer vision. CNNs are not limited to images only. You’ll find them everywhere now, from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes inevitable in all the fields of data science. Even most of the Recurrent Neural Networks (RNNs) rely on CNNs nowadays.
- In this section, you will to learn about:
8. Recurrent Neural Networks with Python
- In this section, you will learn about:
- The significance of Recurrent Neural Networks (RNNs) in data science.
- The reasons to shift to RNNs from classical sequence models.
- The important concepts from the absolute beginning with comprehensive unfolding with examples in Python.
- Practical explanation and live coding with Python.
- Intricate details of RNNs with examples and derivations.
- The use and applications of RNNs (with implementations in framework TensorFlow) that are more recent and advanced.
- Building your own applications for automatic text generation as well as for stock price prediction.
9. Reinforcement Learning
- The training data is not always available, and the learner may have to learn through experience. This, in fact, is true in most cases, particularly when the learner is acting in an uncertain and non-stationary environment.
- The learner needs to act in real-time and, hence, has to decide its action in real-time in response to the environment (a self-driving car, for example).
- Reinforcement Learning (RL) brings its own challenges, and its solutions sometimes are much different than the solutions offered by supervised as well as unsupervised learning.
- Large scale RL problems do require the knowledge of ANNs to model the problem.
- Reinforcement Learning is considered a game-changer in the field of data science, particularly after observing the winnings of CHESS and GO against human champions. However, RL is not restricted to games only. RL is everywhere now, ranging from Recommender Systems to more advanced applications in stock prediction. So, an understanding of RL becomes inevitable in all the fields of data science.
- In this section, you will learn about:
- The importance of Reinforcement Learning (RL) in data science.
- The key concepts from the absolute beginning with complete unfolding with examples in Python.
- Practical explanation and live coding with Python.
- Applications of Probability Theory.
- Markov Decision Processes.
1. Python for Data Science and Data Analysis
- You start with problem-solving and finish with fancy indexing and plots in Matplotlib.
- No prior knowledge in any computer science language is assumed.
- Great fun with Python language.
-
Reasonable treatment of data science packages (NumPy, Pandas, Matplotlib, Seaborn, and Sklearn).
-
After this course, you will be a competent Python programmer as well as a reasonable expert of data science packages (NumPy, Pandas, Matplotlib).
-
This section is designed to teach you programming in general also. Therefore, shifting from this language to any other language after this section is not difficult.
3. Mastering Probability and Statistics in Python
- Obviously, concepts in data science are not new. In fact, it is also believed that data science is merely a renamed version of Probability and Statistics. Well, without being biased to that extent, we will say that the practical nature of applications was uncovered earlier even though the theory traces back to Probability and Statistics.
- One way or the other, knowing Probability and Statistics makes a significant theoretical as well as practical difference.
- Most of the courses on Probability and Statistics, however, fail to link the data science practices and theory by merely focusing on the axiomatic treatment of the subject.
-
We build this section by keeping the practical needs of data science in mind as well as the importance of theory.
- Wherever important, we deliberately explain and show the relationships by derivations and even through Python Code.
-
This section builds a very sound basis for understanding the classical concepts in data science as well as its more recent generalizations.
-
We start with the very basics of Probability, go through inference and estimations, link famous machine learning techniques with conditional probability, and finally, show that Deep Neural Networks indeed learn a probability function eventually.
5. Feature Engineering and Dimensionality Reduction with Python
- Knowing the sections you have covered thus far certainly brings you a huge clarity of the field. But there is still one thing that brings the improvements in the results with a reasonable margin, and that is data preprocessing or data preparation.
- Most of the data science today relies on preparing the data suitable for machine learning models. An effective way of data preparation, most of the time, becomes a game-changer.
- This section focuses on data preparation for machine learning models.
- We build this section to provide an understanding of why selecting features and transforming features are important.
- We also discuss practical issues with real data, like missing values and non-numeric data.
-
We discuss the performance improvements both in terms of execution time as well as the accuracy of the models.
-
We explain the required mathematical background in a simple way.
-
Finally, all the concepts are made more easily understandable by coding relevant examples in Python.
7. Convolutional Neural Networks with Python
- ANNs, in its most basic form, is not that suitable for image data and for the problems in computer vision.
- Convolutional Neural Networks (CNNs) are considered a game-changer in the field of computer vision. CNNs are not limited to images only. You’ll find them everywhere now, from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes inevitable in all the fields of data science. Even most of the Recurrent Neural Networks (RNNs) rely on CNNs nowadays.
- In this section, you will to learn about:
2. Data Understanding and Data Visualization with Python
- This section deals with the in-depth treatment of data science packages both for data manipulation as well as data visualization.
- While Section 1 focuses more on Python language, this section focuses completely on data science packages and their efficient use.
- The packages covered in this section include NumPy, Pandas, Matplotlib, Seaborn, Bokeh, Plotly, and Folium.
-
As far as we know, this is the most comprehensive section on data understanding and visualization among the available ones.
-
Further, this section is designed to reduce the dependency on core Python language to be treated independently, as well.
-
2D and 3D visualizations, interactive visualizations, and geographic maps are also covered.
-
Proceeding in data science with being able to effectively play with the data using famous packages makes progress much worse, and this section addresses this concern.
4. Machine Learning Crash Course
- Although several concepts, or even all, fall under the umbrella of Probability and Statistics, it turns out that most of the concepts have made their own practical place, mostly derived through engineering, with the name of Machine Learning. For example, the term “overfitting” is now referring to the area of machine learning.
- Machine Learning brings its own set of practices to reach the demands of automation. Hence, mastering these concepts becomes inevitable.
- This section is actually a quick walkthrough of the concepts in Machine Learning and focuses on all the theoretical as well as practical concepts.
-
We mostly cover applications using the Sklearn Python package and build machine learning pipelines in this section.
- We also elaborate on more advanced areas of machine learning, which we later present as separate sections.
6. Artificial Neural Networks with Python
8. Recurrent Neural Networks with Python
- In this section, you will learn about:
9. Reinforcement Learning
- The training data is not always available, and the learner may have to learn through experience. This, in fact, is true in most cases, particularly when the learner is acting in an uncertain and non-stationary environment.
- The learner needs to act in real-time and, hence, has to decide its action in real-time in response to the environment (a self-driving car, for example).
- Reinforcement Learning (RL) brings its own challenges, and its solutions sometimes are much different than the solutions offered by supervised as well as unsupervised learning.
- Large scale RL problems do require the knowledge of ANNs to model the problem.
- Reinforcement Learning is considered a game-changer in the field of data science, particularly after observing the winnings of CHESS and GO against human champions. However, RL is not restricted to games only. RL is everywhere now, ranging from Recommender Systems to more advanced applications in stock prediction. So, an understanding of RL becomes inevitable in all the fields of data science.
- In this section, you will learn about:
- The importance of Reinforcement Learning (RL) in data science.
- The key concepts from the absolute beginning with complete unfolding with examples in Python.
- Practical explanation and live coding with Python.
- Applications of Probability Theory.
- Markov Decision Processes.
After completing this course successfully, you will be able to:
- Relate the concepts, principles, and theories in Data Science & Machine Learning.
- Understand the methodology of Data Science & Machine Learning using real datasets.
FAQ
Is there a certificate of completion
Yes Absolutely, You will obtain an AI Sciences certificate on data science and machine learning.
How long will this training take
At AI Sciences, we know that not all students progress in the same way. Therefore, although the course can be completed in one month, we will work with students individually if they so wish to define a schedule for them to follow so that they master all the points in the course.
Are there any prerequisites to start this course?
No prior knowledge is needed only your sincere willingness to learn and practice.
You will start with the basic concepts and gradually build your knowledge of the subject until you will master Data Science and Machine Learning.
How Is This Course Different?
AI Sciences Team strongly believes in learning by doing methodology. With this in mind, we have crafted this course with care. You will find that the emphasis on the theoretical aspects of machine learning is equal to the emphasis on the practical aspects of the subject matter.
Each lesson presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques.
The course offer you all the following benefits:
- Access to over 700+ brief HD videos along with detailed code notebooks
- Practical with live coding.
- Helpful homework and Hands-on Projects with real-life data
- Live Q&A sessions with your Instructors every week
- Achievement Certificate
You only need access to a computer with the internet to get started.
Some testimonials
members of the training
The decision is now
Are You Ready To Get Off The Course Mastering The A-Z Of Data Science & Machine Learning. Click On The Button Below To Get 87% Off The Actual Price.
$2,999 $399
© 2021, AI Sciences OÜ. All trademarks and registered trademarks appearing on aisciences.io are the property of their respective owners.
