Overview:
If you want to get some portion of billions of dollars coming in AI in the near future, this course is for you.
Welcome to our Level-2 Advanced Data Science and Machine Learning course tailored for aspiring AI professionals. This comprehensive program delves deeper into the intricacies of data science and machine learning algorithms, equipping you with the advanced skills needed to excel in the rapidly evolving field of artificial intelligence.
Freelance Rates:
- Freelance Platforms: Explore freelance opportunities in data science and machine learning projects on popular platforms such as Upwork, Freelancer, and Fiverr.
- Rate Comparison:
- Pakistan: $20 per hour / $800 per month
- India: $25 per hour / $1000 per month
- United States: $100 per hour / $4000 per month
- United Kingdom: £80 per hour / £3200 per month
- Europe: €90 per hour / €3600 per month
- China: ¥400 per hour / ¥16000 per month
- Rest of Europe: €85 per hour / €3400 per month
Curriculum
- 12 Sections
- 48 Lessons
- 8 Weeks
Expand all sectionsCollapse all sections
- Demo Class1
- Pure Data Science And Machine Learning Practice5
- Module 1: Foundations of Data Science and Machine Learning5
- Module 2: Advanced Machine Learning Algorithms5
- Module 3: Deep Learning Fundamentals5
- Module 4: Natural Language Processing (NLP)5
- Module 5: Reinforcement Learning5
- Module 6: Model Deployment and Productionization5
- Module 7: Advanced Topics in Data Science and AI5
- Module 8: Capstone Project4
- Exam3
- Build Your CV0
This course is designed for data science professionals, machine learning engineers, AI researchers, software engineers, graduate students, entrepreneurs, and anyone interested in advancing their skills in data science and machine learning.
Participants should have a basic understanding of data science concepts, programming languages such as Python, R, or Julia, and mathematical fundamentals including calculus, linear algebra, and probability theory.
While some familiarity with data science and machine learning concepts is recommended, beginners with a strong desire to learn and willingness to dedicate time to study can benefit from this course.
The course offers a combination of live online sessions, self-paced modules, hands-on projects, and interactive workshops, providing flexibility to accommodate diverse learning styles and schedules.
The time commitment varies depending on your learning pace and schedule. On average, participants can expect to dedicate a few hours per week to attend live sessions, complete assignments, and engage in self-study.
Yes, participants will have access to online forums, networking events, and exclusive membership groups where they can connect with peers, industry experts, and alumni to exchange ideas, collaborate on projects, and expand their professional network.
Yes, participants who successfully complete the course requirements will receive a recognized certificate, validating their proficiency in advanced data science and machine learning concepts.
Yes, participants will receive career guidance, resume review, interview preparation, and job placement assistance to help them transition into roles in data science, machine learning engineering, AI research, and related fields.
Yes, participants will have access to updated course materials, resources, and industry insights to support continuous learning and stay abreast of the latest advancements and trends in data science, machine learning, and artificial intelligence.
This course stands out for its advanced curriculum, hands-on projects, expert instruction, flexible learning options, career development support, networking opportunities, and continuous learning resources, providing a comprehensive learning experience tailored to the needs of aspiring AI professionals.
Requirements
- Foundational Knowledge: Basic understanding of data science concepts, including data manipulation, statistical analysis, and machine learning fundamentals. Familiarity with programming languages such as Python, R, or Julia. Proficiency in mathematical concepts such as calculus, linear algebra, and probability theory.
- Hardware and Software Requirements: A personal computer or laptop with internet access. Installation of necessary software packages and development environments, including Python (with libraries such as NumPy, Pandas, Scikit-learn), R, or Julia. Access to cloud computing platforms like AWS, Google Cloud Platform, or Microsoft Azure for deploying machine learning models (optional but recommended).
- Educational Background: Recommended for participants with a bachelor's degree or equivalent experience in a related field such as computer science, mathematics, statistics, engineering, or physics. While not mandatory, prior coursework or practical experience in data science, machine learning, or artificial intelligence is advantageous.
- Time Commitment: Dedication to attend live sessions or complete self-paced modules as per the course schedule. Allocation of sufficient time for independent study, practice assignments, and project work outside of class hours.
- Language Proficiency: Proficiency in English to comprehend course materials, participate in discussions, and submit assignments.
- Access to Learning Resources: Access to recommended textbooks, online tutorials, and supplementary resources to enhance learning outcomes. Availability of a reliable internet connection to access course materials, participate in virtual lectures, and engage with the learning community.
- Commitment to Learning: Willingness to actively participate in class discussions, ask questions, and seek clarification on challenging topics. Motivation to continuously learn, adapt, and apply newly acquired skills in practical settings.
Features
- Advanced Curriculum: A comprehensive curriculum designed by industry experts, covering advanced topics in data science and machine learning, including deep learning, natural language processing, reinforcement learning, and more.
- Hands-on Projects: Engage in practical, project-based learning experiences that simulate real-world scenarios, allowing you to apply theoretical concepts to solve complex problems and build a robust portfolio of AI projects.
- Expert Instruction: Learn from seasoned professionals and experienced instructors with extensive expertise in data science, machine learning, and artificial intelligence, who provide personalized guidance and mentorship throughout the course.
- Live Sessions and Workshops: Participate in live lectures, interactive workshops, and Q&A sessions conducted by industry practitioners, enabling you to deepen your understanding, ask questions, and collaborate with peers in real-time.
- Flexible Learning Options: Choose between flexible learning formats, including live online sessions, self-paced modules, and recorded lectures, to accommodate diverse schedules and learning preferences.
- Practical Tools and Techniques: Gain proficiency in the latest tools, libraries, and frameworks used in data science and machine learning, including Python, TensorFlow, PyTorch, scikit-learn, and more, through hands-on coding exercises and demonstrations.
- Career Development Support: Receive career guidance, resume review, interview preparation, and job placement assistance to help you transition into roles in data science, machine learning engineering, AI research, and related fields.
- Networking Opportunities: Connect with a global community of like-minded professionals, industry experts, and alumni through online forums, networking events, and exclusive membership groups, fostering collaboration and knowledge sharing.
- Continuous Learning: Access to updated course materials, resources, and industry insights to stay abreast of the latest advancements and trends in data science, machine learning, and artificial intelligence throughout your career.
- Certification: Earn a recognized certification upon successful completion of the course, validating your proficiency and expertise in advanced data science and machine learning concepts, enhancing your credibility and marketability in the industry.
Target audiences
- Data Science Professionals: Experienced data scientists seeking to deepen their understanding of advanced data science techniques and machine learning algorithms to stay competitive in the field.
- Machine Learning Engineers: Professionals working in machine learning engineering roles who wish to expand their knowledge and skills in developing sophisticated AI models and deploying them at scale.
- AI Researchers: Individuals engaged in AI research who want to enhance their expertise in cutting-edge methodologies and approaches in data science and machine learning.
- Software Engineers: Software developers interested in transitioning into roles focused on AI and machine learning, aiming to broaden their skill set and explore opportunities in this rapidly growing field.
- Graduate Students: Postgraduate students pursuing degrees in computer science, statistics, mathematics, or related disciplines, looking to complement their academic studies with practical, industry-relevant skills.
- Professionals in Related Fields: Professionals from diverse backgrounds such as finance, healthcare, marketing, and engineering, seeking to leverage data science and machine learning techniques to solve complex problems and drive innovation in their respective domains.
- Entrepreneurs and Business Owners: Business leaders and entrepreneurs aiming to harness the power of AI and data-driven decision-making to optimize processes, enhance products, and gain a competitive edge in the market.
- Career Changers: Individuals considering a career transition into data science, machine learning, or AI, who are eager to acquire the necessary knowledge and skills to pursue rewarding opportunities in these fields.
- Life-long Learners: Enthusiastic learners passionate about emerging technologies and eager to explore the frontiers of AI and machine learning, regardless of their professional background or current career stage.
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